巴西里约热内卢Niteroi物业评估工程检验相关人工智能

Vladimir Surgelas, I. Arhipova, V. Pukite
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引用次数: 0

摘要

建筑业与一个国家的总体发展息息相关。有很多数据是分散的,并且没有适当地探索与所建造的建筑物有关的数据。然而,如果将这些关于房地产市场行为的分散数据组织起来,并结合土木工程知识,这种信息的合并可以缓解一些评估问题,特别是那些由于未知或可疑原因而被高估的问题。因此,需要能够处理有限数据的模型来分析解释变量和销售价格之间的因果关系,并从中预测房地产价值。本文的目的是创新地使用简单的建筑检验策略来预测住宅公寓的市场价格。为此,于2021年2月在巴西里约热内卢的Niterói市使用了19个住宅公寓样本。该方法使用土木工程调查的结果,并将其转换为启发式术语,预测财产的价格。因此,人类表达的不精确性、不确定性和主观性与土木工程知识相结合,产生了一个合理的解决方案,并易于在市场上应用。最后,除了避免二元逻辑中回归系数的重复外,在性质评价中使用模糊逻辑是一种适当的非常规方法。为了检验该方法的可靠性,将样本的市场价值与模糊逻辑预测的价值进行了比较。根据平均绝对百分比误差(MAPE)的结果可以解释为良好的结果(7%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Engineering Inspection Associated Artificial Intelligence for Appraisal of the Property in Niteroi, Rio de Janeiro, Brazil
The construction sector is linked to the general development of a country. There is a lot of data scattered and not properly explored in relation to the buildings constructed. However, if these scattered data on the behavior of the real estate market are organized, combined with knowledge of civil engineering, this merger of information can mitigate some evaluation problems, especially those that are overvalued for unknown or dubious reasons. Thus, there is a need for models capable of working with limited data to analyze the causal relationships between explanatory variables and sales prices and, from there, predict property values. The purpose of this article is the innovative use of simple building inspection strategies to predict the market price for residential apartments. For this, 19 samples of residential apartments are used in the city of Niterói, Rio de Janeiro, Brazil, in February 2021. The methodology uses the results of the survey of civil engineering and converts them into heuristic terms predicting the price of the property. With this, the imprecision, uncertainty, and subjectivity of human expression combined with the knowledge of civil engineering result in a plausible solution and easy application in the market. Finally, the use of fuzzy logic in the evaluation of properties is an adequate unconventional method, in addition to avoiding repetition in regression coefficients in binary logic. To check the reliability of the method, the comparison between the market values of the samples and the values predicted by the fuzzy logic is used. The result according to the mean absolute percentage error (MAPE) can be interpreted as a good result (7%).
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