Housing market analysis using a hierarchical–spatial approach: the case of Belo Horizonte, Minas Gerais, Brazil

Abstract

The paper analyzes the determinants of apartments' prices in Belo Horizonte, MG, Brazil, with the use of hierarchical models, spatial models and a hierarchical-spatial approach. Besides the apartments' characteristics, such as area, age and building standard, prices were determined by local urban amenities. The hierarchical models indicated that local variables, such as urban violence, infrastructure and services, explained over 75% of prices' remaining variability. The spatial models analyzed if, after controlling for price variability with the explanatory variables in the second level of the hierarchical models, spatial correlations still existed in price determination. The positive and significant spatial coefficient in spatial autoregressive models (SAR) indicated spatial dependency. The hierarchical-spatial approach showed that over 70% of apartment prices' remaining variability could be explained by local variables and that the lagged urban services variable explained another 12% of this variability.

A Survey of Methods and Input Data Types for House Price Prediction

“…A hierarchical-spatial approach is proposed in [63] by combining spatial econometrics with a two-level approach for apartment price prediction: the first level relates to the individual apartment characteristics, and the second level includes local neighborhood characteristics. Similarly, a multilevel linear regression model is implemented in [78]; however, a conditional autoregressive term is added instead of the general spatial model that forms the basis of the multilevel model used in [63].…”

Housing Prices Prediction with a Deep Learning and Random Forest Ensemble

“…In spite of being the largest real state market in Latin America, with a demand for about 7.77 million new housing units as of 2017 [Associação Brasileira de Incorporadoras Imobiliárias -ABRAINC 2017], the studies about this market in the country [Moreira de Aguiar et al 2014, De Souza 1999 do not report the use of ML algorithms to predict housing prices of Brazilian properties for sale and rental. To leverage this promising under-researched topic, this work aims to combine supervised ML methods, Natural Language Processing (NLP) models, and Data Science (DS) techniques to predict housing prices in Brazil.…”