论文标题
PATE：房地产，设施，交通和情感共同进行房地产价格预测
PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction
论文作者
论文摘要
房地产价格对个人，家庭，企业和政府有重大影响。房地产价格预测的一般目标是识别和利用由多个方面的房地产交易产生的社会经济模式，从物业本身到其他促成因素。但是，价格预测是一个具有挑战性的多维问题，涉及估计财产本身以外的许多特征。在本文中，我们使用多种数据来源来评估不同社会经济特征的经济贡献，例如周围的设施，交通状况和社会情绪。我们的实验是在中国北京的28,550所房屋上进行的，我们以其重要性对每个特征进行了排名。由于使用多源信息可以提高预测的准确性，因此上述特征可以是评估房地产经济和社会价值的宝贵资源。代码和数据可在以下网址获得：https：//github.com/indigopurple/pate
Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multi-source information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE
