Authors: Felipe Valdez*, Northern Illinois University, Xuwei Chen, Northern Illinois University
Topics: Urban Geography, Spatial Analysis & Modeling, Latin America
Keywords: housing prices, residential choices, spatial regression, geographic weighted regression, Ecuador
Session Type: Paper
Start / End Time: 2:00 PM / 3:40 PM
Room: Balcony A, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Housing prices are determined by a variety of factors, ranging from structural attributes to neighborhood characteristics. Those factors represent not only their relations with the housing market but also individual residential preferences. The spatial variation of housing prices embodies specific local characteristics that explain housing choice complexity. Although housing price has been widely studied in this context, limited research has focused on Ecuadorian cities. This study explores the spatial variation of housing price from a comprehensive approach in order to understand specific local property values and relate them with other urban phenomena such as residential segregation and spatial justice. Using georeferenced cadaster property datasets and census data at the block level, this study tests different spatial regression techniques such as spatial error, spatial autoregressive models and Geographic Weighted Regression to analyze the spatial patterns of housing price and how relevant determinants affect housing prices in Quito, Ecuador. The analyses found that spatial regressions are more effective at capturing the varying effects of the determinants on housing price than the global forms. The results also demonstrated how those factors, along with housing price, affect residential selection. This spatial heterogeneity of housing price determinants suggests that local specificities need to be included to better explain housing markets as well as to effectively regulate and plan the city.