Open AccessProperty evaluation based on ambiguous logic through building inspection in São Paulo city, Brazil
Author(s) -
Vladimir Surgelas,
AUTHOR_ID,
Vivita Pukite,
Irina Arhipova,
AUTHOR_ID
Publication year - 2021
Publication title -
research for rural development/research for rural development (online)
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.212
H-Index - 9
eISSN - 2255-923X
pISSN - 1691-4031
DOI - 10.22616/rrd.27.2021.041
Subject(s) - mean absolute percentage error , real estate , mean squared error , apartment , fuzzy logic , statistics , subjectivity , mathematics , computer science , artificial intelligence , engineering , civil engineering , economics , philosophy , finance , epistemology
The civil engineering branch is strongly related to the development of the countries and there is still a lot of information available in the buildings constructed. However, these data are dispersed without proper treatment. On the other hand, if these real estate data are reorganized to discover behavior parameters, these properties’ values can be predicted and still work as data and causal relationships between explanatory variables. The purpose of the research is to use construction inspection strategies associated with artificial intelligence to predict the market value of a residential apartment. In this academic experiment, only 6 samples of residential apartments are used. Those samples are located in the Lithuania Republic square at Vila Zelina neighborhood, in São Paulo, Brazil, a source in February 2021. The method uses the results of the inspection of civil engineering and converts them into linguistic terms. The result considers the imprecision, uncertainty, and subjectivity of human expression combined with artificial intelligence and civil engineering. To test the feasibility of the process, a comparison is made between the market values of the samples and the values predicted by the Fuzzy logic. Thus, the good results derived a Main Percentage Absolute Error (MAPE) of 5%, the mean absolute error (MAE), root-mean-square error (RMSE), and determination coefficient (R2) of 0.99.