# RECORD ba50780b6392394b1c447f0f41a74dda6abb3b88
# TITLE: USE OF SPATIAL REGRESSION MODELS IN THE MASS APPRAISAL OF REAL ESTATE IN THE COASTAL NEIGHBORHOODS OF MACEIÓ, ALAGOAS, BRAZIL
# YEAR: 2025.0  VENUE: Revista Políticas Públicas & Cidades  DOI: 10.23900/2359-1552v14n8-21-2025
# AUTHORS: Carlos Alberto Peruzzo Trivelloni; Nelson Marisco; Norberto Hochheim
# PAGES: 27  CANDIDATE TABLE PAGES (1-based): []
# ABSTRACT: This paper presents the results obtained in the research that has been carried out to study the price of apartments in the coastal neighborhoods of the city of Maceió, State of Alagoas, Brazil: Ponta Verde, Cruz das Almas, Jatiúca, Pajuçara. The research studies the price of real estate in these seaside neighborhoods as support for understanding the value of the square meter practiced. This research uses a comparative method of market data using spatial econometric models supported by Geographic Information Systems (GIS). The data on the properties on offer was collected from real estate agents and from property listings on the Internet. It was then systematized in electronic spreadsheets, with the plane coordinates georeferenced to the UTM system and the information imported into QGis. The computer programs GeoDa and GeoDaSpace were used to carry out Exploratory Spatial Data Analysis and classical and spatial regressions for the estimation process. The results obtained show that the Spatial Lag (SAR) model using two-stage spatially generalized Instrumental Variables (IV) estimators (S2SLS + MGM/HAC) was able to eliminate the effects of spatial autocorrelation of the residuals present in the classic linear regression model. The exogenous and endogenous variables used to compose the model were statistically significant. These variables characterize the surrounding information, the characteristics of the properties and the socio-economic aspects of the neighbourhood. The results show that apartment prices are more influenced by the variables garage, sea view, floor area, apartment age, average per capita household income and land occupation rate.


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Revista PPC – Políticas Públicas e Cidades 
ISSN: 2359-1552 
 
Pag. 1 
Revista PPC – Políticas Públicas e Cidades, Curitiba, v.14, n.8, p. 01-27, 2025. 
Artigo 
 
USE OF SPATIAL REGRESSION MODELS IN THE MASS APPRAISAL OF 
REAL ESTATE IN THE COASTAL NEIGHBORHOODS OF MACEIÓ, 
ALAGOAS, BRAZIL 
 
USO DE MODELOS DE REGRESSÃO ESPACIAL NA AVALIAÇÃO EM 
MASSA DE IMÓVEIS NOS BAIRROS LITORAL DE MACEIÓ, ALAGOAS, 
BRASIL 
 
USO DE MODELOS DE REGRESIÓN ESPACIAL EN LA EVALUACIÓN 
MASIVA DE INMUEBLES EN LOS BARRIOS COSTEROS DE MACEIÓ, 
ALAGOAS, BRASIL 
 
Carlos Alberto Peruzzo Trivelloni 
PhD in Civil Engineering, Specializing in Multipurpose Registry and Territorial 
Management, Universidad de la República (UDELAR), Montevideo, Uruguay. 
E-mail: carlos.peruzzo@gmail.com 
 
Nelson Marisco 
PhD in Civil Engineering, Specializing in Multipurpose Registry and Territorial 
Management, Universidade Federal de Alagoas, Maceió, Alagoas, Brazil. 
E-mail: nelson.marisco@ceca.ufal.br 
 
Norberto Hochheim 
PhD in Economic Engineering from the University of Nancy I - France, 
Universidade de Santa Catarina, Florianópolis, Santa Catarina, Brazil. 
E-mail: norberto.hochheim@ufsc.br 
 
ABSTRACT 
This paper presents the results obtained in the research that has been carried 
out to study the price of apartments in the coastal neighborhoods of the city of 
Maceió, State of Alagoas, Brazil: Ponta Verde, Cruz das Almas, Jatiúca, 
Pajuçara. The research studies the price of real estate in these seaside 
neighborhoods as support for understanding the value of the square meter 
practiced. This research uses a comparative method of market data using spatial 
econometric models supported by Geographic Information Systems (GIS). The 
data on the properties on offer was collected from real estate agents and from 
property listings on the Internet. It was then systematized in electronic 
spreadsheets, with the plane coordinates georeferenced to the UTM system and 
the information imported into QGis. The computer programs GeoDa and 
GeoDaSpace were used to carry out Exploratory Spatial Data Analysis and 
classical and spatial regressions for the estimation process. The results obtained 
show that the Spatial Lag (SAR) model using two-stage spatially generalized 
Instrumental Variables (IV) estimators (S2SLS + MGM/HAC) was able to 
eliminate the effects of spatial autocorrelation of the residuals present in the 
                                                          
DOI: https://doi.org/10.23900/2359-1552v14n8-21-2025 
Submitted on: 9.29.2025 | Accepted on: 10.2.2025 | Published on: 10.17.2025