1# RECORD ba50780b6392394b1c447f0f41a74dda6abb3b882# TITLE: USE OF SPATIAL REGRESSION MODELS IN THE MASS APPRAISAL OF REAL ESTATE IN THE COASTAL NEIGHBORHOODS OF MACEIÓ, ALAGOAS, BRAZIL3# YEAR: 2025.0 VENUE: Revista Políticas Públicas & Cidades DOI: 10.23900/2359-1552v14n8-21-20254# AUTHORS: Carlos Alberto Peruzzo Trivelloni; Nelson Marisco; Norberto Hochheim5# PAGES: 27 CANDIDATE TABLE PAGES (1-based): []6# 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.789===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) =====1011 12 13Revista PPC – Políticas Públicas e Cidades 14ISSN: 2359-1552 15 16Pag. 1 17Revista PPC – Políticas Públicas e Cidades, Curitiba, v.14, n.8, p. 01-27, 2025. 18Artigo 19 20USE OF SPATIAL REGRESSION MODELS IN THE MASS APPRAISAL OF 21REAL ESTATE IN THE COASTAL NEIGHBORHOODS OF MACEIÓ, 22ALAGOAS, BRAZIL 23 24USO DE MODELOS DE REGRESSÃO ESPACIAL NA AVALIAÇÃO EM 25MASSA DE IMÓVEIS NOS BAIRROS LITORAL DE MACEIÓ, ALAGOAS, 26BRASIL 27 28USO DE MODELOS DE REGRESIÓN ESPACIAL EN LA EVALUACIÓN 29MASIVA DE INMUEBLES EN LOS BARRIOS COSTEROS DE MACEIÓ, 30ALAGOAS, BRASIL 31 32Carlos Alberto Peruzzo Trivelloni 33PhD in Civil Engineering, Specializing in Multipurpose Registry and Territorial 34Management, Universidad de la República (UDELAR), Montevideo, Uruguay. 35E-mail: carlos.peruzzo@gmail.com 36 37Nelson Marisco 38PhD in Civil Engineering, Specializing in Multipurpose Registry and Territorial 39Management, Universidade Federal de Alagoas, Maceió, Alagoas, Brazil. 40E-mail: nelson.marisco@ceca.ufal.br 41 42Norberto Hochheim 43PhD in Economic Engineering from the University of Nancy I - France, 44Universidade de Santa Catarina, Florianópolis, Santa Catarina, Brazil. 45E-mail: norberto.hochheim@ufsc.br 46 47ABSTRACT 48This paper presents the results obtained in the research that has been carried 49out to study the price of apartments in the coastal neighborhoods of the city of 50Maceió, State of Alagoas, Brazil: Ponta Verde, Cruz das Almas, Jatiúca, 51Pajuçara. The research studies the price of real estate in these seaside 52neighborhoods as support for understanding the value of the square meter 53practiced. This research uses a comparative method of market data using spatial 54econometric models supported by Geographic Information Systems (GIS). The 55data on the properties on offer was collected from real estate agents and from 56property listings on the Internet. It was then systematized in electronic 57spreadsheets, with the plane coordinates georeferenced to the UTM system and 58the information imported into QGis. The computer programs GeoDa and 59GeoDaSpace were used to carry out Exploratory Spatial Data Analysis and 60classical and spatial regressions for the estimation process. The results obtained 61show that the Spatial Lag (SAR) model using two-stage spatially generalized 62Instrumental Variables (IV) estimators (S2SLS + MGM/HAC) was able to 63eliminate the effects of spatial autocorrelation of the residuals present in the 64 65DOI: https://doi.org/10.23900/2359-1552v14n8-21-2025 66Submitted on: 9.29.2025 | Accepted on: 10.2.2025 | Published on: 10.17.2025 67