There are various factors affecting flat sales prices. Various characteristics of a flat play an important role in determining its sales price. In this study, a machine learning based Bayesian network was built by a restrictive structural learning algorithm using the data collected from 24 randomly selected cities in Turkey. The data consist of the sales prices and various characteristics of a flat such as number of bedrooms, building age, availability of balcony, net area, heating type, mortgageability, number of bathrooms, seller type, presence in a housing estate area and floor location. After the model validity check, a sensitivity analysis was performed for the estimated Bayesian network model and related results were provided. Some of these results indicate that sales prices of flats mostly change depending on the number of bathrooms available. Additionally, number of bedrooms, net area and floor location are also determinative about the sales prices. The lack of significant difference among the sales prices of flats that are sold by construction companies, housing estate agents or property owners is another result obtained.

有多种因素影响固定销售价格。公寓的各种特性在确定其销售价格中起着重要作用。在这项研究中，使用限制性结构学习算法，使用从土耳其24个随机选择的城市收集的数据，构建了基于机器学习的贝叶斯网络。数据包括销售价格和公寓的各种特征，例如卧室数量，建筑年龄，阳台的可用性，净面积，供暖类型，抵押能力，浴室数量，卖方类型，居住区和楼层位置。在模型有效性检查之后，对估计的贝叶斯网络模型进行了敏感性分析，并提供了相关结果。其中一些结果表明，公寓的销售价格主要取决于可用浴室的数量而变化。此外，卧室的数量，净面积和地板位置也决定了销售价格。由建筑公司，房屋中介或业主出售的公寓的售价之间没有显着差异是另一个结果。