Construction of Training Data for Price Prediction of a Real Estate from Internet Ads

Proceedings of the International Scientific Conference - Sinteza 2019 Pub Date : 1900-01-01 DOI:10.15308/SINTEZA-2019-388-393

Mladen Vidović, Ivan Radosavljević, Aleksandra Mitrovic, Z. Konjovic, Dobrivoje Đurić, V. Matic, M. Simić, Miljan Vucetic, Gardelito HewAKee, Miloš Stanković, Kristina Kaličanin, Milica Čolović, A. Njeguš, V. Mitić, Aleksa Ćuk, Branivoj Miljković, Miloš Todorović, Aleksandar Ivanović, M. Zivkovic, E. Mele, Marlene Gröblacher, Vule Mizdraković, Danica Rajin, Marijana Petrović, Tijana Radojević, Ričardas Butėnas, Zlata Bracanović, Nemanja Bošnjak, Milica Peric, M. Stanišić, Nikica Radović, J. Nikolic, B. Vakanjac, L. Amidžić, Tanita Đumić, Vladimir Mirković, Jelena Lukic, Vesna Martin, I. Miljković, M. Dobrojevic, J. Pršić, Vesna Ristić Vakanjac, M. Trkulja, M. Ilić, S. D. Milošević, Nikola Dražić, J. Milovanovic, G. Dražić, E. Marišová, Maja Gligorić, Marija Kostić, J. Gržinić, E. Pap, M. Petković, Ana Blagojević, S. Stanišić, Jelena Teodorović, Slađana Čabrilo

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引用次数: 1

Abstract

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, attractiveness 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 convolution neural network for detecting interior facilities from real estate photos.

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基于网络广告的房地产价格预测训练数据构建

本文提出了一个模型，用于构建一个数据集，旨在从标准的互联网广告中预测房地产(房屋和公寓)的价格。预测房地产价格的模型除了包括出现在广告中的标准房地产的特征(面积，卧室数量等)外，还包括房地产位置的吸引力以及一些额外的室内设施(例如，冰箱，洗碗机，炉子等)的信息。所提出的训练集构建模型使用OpenStreetMap的天桥API来确定房地产位置的吸引力，并使用卷积神经网络从房地产照片中检测内部设施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the International Scientific Conference - Sinteza 2019

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