Value Estimation of Properties Administered by the Brazilian Army Using Machine Learning and Spatial Components
Resumo
The valuation of an institution’s patrimony represents a necessary condition for an efficient management of its assets. The execution and analysis of real estate appraisal reports are essential to the achievement of some strategic objectives of the Brazilian Army, but they are also quite costly in terms of time, labor and financial resources. Sometimes, great effort is required for the aforementioned steps to take place and the market value finally obtained is inconsistent with what was initially imagined by the authorities, causing the technical study carried out to not be effectively used in negotiations by the organization. This work proposes the development of predictive models capable of building estimates of real estate values, so that the formal requests of the managers that imply the stages of execution and analysis of appraisal reports can occur with this information as an initial input. Counting on linear and nonlinear approaches and on machine learning techniques, the models have a reasonable level of assertiveness and national geographic coverage when generate estimated market values of Union real estate assets. Intrinsic and extrinsic variables to the properties were considered, including tests of aggregation of spatial components on some of them. As the interpretability of the proposed solution is an important requirement in both linear and nonlinear approaches, the Shapley value was adopted as a tool to support the guarantee of explainability and a PLS-SEM conceptual model was built to select attributes in a reasoned manner. These two considerations associated with modeling of real estate prices at a national level represent an innovation of this work in relation to the scientific literature analyzed.
Palavras-chave:
data mining, machine learning, real estate, spatial components
Referências
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Barros Antunes Campos, R. and Almeida, E. Decomposição espacial nos preços residenciais no município de São Paulo. Estudos Econômicos (São Paulo) 48 (1): 5–38, 2018.
Dewan, P., Ganti, R., Srivatsa, M., and Stein, S. NN-SAR: A Neural Network Approach for Spatial AutoRegression. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Kyoto, Japan, pp. 783–789, 2019.
Furtado, B. A. NT DISET 78 - Gerando Famílias Artificiais Intraurbanas: censo 2010. Ipea, 2020.
Hagenauer, J. and Helbich, M. A geographically weighted artificial neural network. International Journal of Geographical Information Science 36 (2): 215–235, 2022.
Hair, J., Hult, G. T. M., Ringle, C., and Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Inc, 2022.
Kiely, T. and Bastian, N. The spatially conscious machine learning model. Statistical Analysis and Data Mining: The ASA Data Science Journal 13 (1): 31–49, 2020.
Lundberg, S., Erion, G., Chen, H., DeGrave, A., Prutkin, J., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence vol. 2, pp. 56–67, 2020.
Park, B. and Bae, J. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications 42 (6): 2928–2934, 2015.
Rosen, S. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy 82 (1): 34–55, 1974.
Tchuente, D. and Nyawa, S. Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research 308 (1-2, SI): 571–608, 2022.
Anselin, L. Spatial Econometrics: Methods and Models. Springer Dordrecht, 1988.
Barros Antunes Campos, R. and Almeida, E. Decomposição espacial nos preços residenciais no município de São Paulo. Estudos Econômicos (São Paulo) 48 (1): 5–38, 2018.
Dewan, P., Ganti, R., Srivatsa, M., and Stein, S. NN-SAR: A Neural Network Approach for Spatial AutoRegression. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Kyoto, Japan, pp. 783–789, 2019.
Furtado, B. A. NT DISET 78 - Gerando Famílias Artificiais Intraurbanas: censo 2010. Ipea, 2020.
Hagenauer, J. and Helbich, M. A geographically weighted artificial neural network. International Journal of Geographical Information Science 36 (2): 215–235, 2022.
Hair, J., Hult, G. T. M., Ringle, C., and Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Inc, 2022.
Kiely, T. and Bastian, N. The spatially conscious machine learning model. Statistical Analysis and Data Mining: The ASA Data Science Journal 13 (1): 31–49, 2020.
Lundberg, S., Erion, G., Chen, H., DeGrave, A., Prutkin, J., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence vol. 2, pp. 56–67, 2020.
Park, B. and Bae, J. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications 42 (6): 2928–2934, 2015.
Rosen, S. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy 82 (1): 34–55, 1974.
Tchuente, D. and Nyawa, S. Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research 308 (1-2, SI): 571–608, 2022.
Publicado
28/11/2022
Como Citar
ALVES DE SOUSA NETO, José Nilo; LADEIRA, Marcelo.
Value Estimation of Properties Administered by the Brazilian Army Using Machine Learning and Spatial Components. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 17-24.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2022.227798.
