Construction of Training Data for Price Prediction of a Real Estate from Internet Ads
Construction of Training Data for Price Prediction of a Real Estate from Internet Ads
Autori:
Izdanje: International Scientific Conference on Information Technology and Data Related Research
DOI: 10.15308/Sinteza-2019-388-393
Oblast: Software and Information Engineering and Internet and Development Perspectives
Stranice: 388-393
Apstrakt:
The paper presents a model for constructing a data set aimed at predicting a price of a real estate (houses and flats) from the standard Internet ads. The model for predicting a real estate price includes, in addition to standard real estate's features (area, number of bedrooms, etc.) appearing in ad, attrac- tiveness of a real estate location as well as information on some additional interior facilities (e.g., refrigerator, dish-washing machine, stove, etc.). The proposed training set construction model uses OpenStreetMap's Overpass API for determining attractiveness of a real estate's location, and a convolu- tion neural network for detecting interior facilities from real estate photos.
Ključne reči: real estate, advertising, price, prediction
Priložene datoteke:
- 388-393 ( veličina: 1,06 MB, broj pregleda: 394 )
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BibTeX format
@article{article, author = {M. Vidović, I. Radosavljević, A. Mitrović and Z. Konjović}, title = {Construction of Training Data for Price Prediction of a Real Estate from Internet Ads}, journal = {International Scientific Conference on Information Technology and Data Related Research}, year = 2020, pages = {388-393}, doi = {10.15308/Sinteza-2019-388-393} }
RT Conference Proceedings A1 Mladen Vidović A1 Ivan Radosavljević A1 Aleksandra Mitrović A1 Zora Konjović T1 Construction of Training Data for Price Prediction of a Real Estate from Internet Ads AD Univerzitet Singidunum, Beograd, Beograd, Srbija YR 2020 NO doi: 10.15308/Sinteza-2019-388-393
M. Vidović, I. Radosavljević, A. Mitrović and Z. Konjović, Construction of Training Data for Price Prediction of a Real Estate from Internet Ads, Univerzitet Singidunum, Beograd, 2020, doi:10.15308/Sinteza-2019-388-393