Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece

Abstract

In this article, geovisualization is used for the presentation and interpretation of spatial analysis results concerning several house attributes. For that purpose, point data for houses in the region of Attica, Greece are analyzed. The data concern houses for sale and comprise structural characteristics, such as size, age and floor, as well as locational attributes. Geovisualization of house characteristics is performed employing spatial interpolation techniques, kriging techniques, in particular. Spatial autocorrelation in the data is examined through the calculation of the Moran’s I coefficient, while spatial clusters of houses with similar characteristics are identified using the Getis-Ord Gi* local spatial autocorrelation coefficient. Finally, a model is developed in order to predict house prices according to several structural and locational characteristics. In that respect, a classic hedonic pricing model is constructed, which is consequently developed as a geographically weighted regression (GWR) model in a GIS environment. The results of this model indicate that two characteristics, i.e., size and age, account for most of the variability in house prices in the study region. Since GWR is a local model producing different regression parameters for each observation, it is possible to obtain the spatial distribution of the regression parameters, which indicate the significance of the house characteristics for price determination in different locations in the study area.

A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece

“…On the other hand, if data with a spatial reference were used, it would be possible to build spatial regression models. These models require a GIS environment, because then the location of the observations could be mapped and it would also be possible to measure spatial autocorrelation [21,26,28,44,45].…”

“…Several aspects of short-term rental activity have been explored, such as the impact on long-term rents for residents, the impact on the neighborhoods hosting short-term rentals, the competition with the hotel sector and the factors influencing short-term rents [13][14][15][16][17][18][19]. However, in Greece, the number of studies on shortterm rentals is very limited [7,8,[20][21][22]. Two of these studies [7,22] Boutsioukis et al [22] examine the Airbnb penetration in the country.…”

“…In that respect, it seems to have a positive impact on both the urban environment and the economy. Iliopoulou and Stratakis [21] explore the spatial distribution of Aibnb rentals in 58 municipalities of the Greater Athens Region and suggest that the presence of hotels and the number of vacant houses are the most important factors for predicting the spatial penetration of Airbnb, while price per sq. m. of houses for sale is the most important factor for explaining the spatial distribution of rents.…”

“…Other POIs concern transportation, often distance from the nearest subway station [23,28,30]. Distance from tourist attractions is important, while the proximity to hotels has been examined as well [21,23,25,31]. Proximity to the coastline also increases rents in coastal Mediterranean cities [29,32].…”

“…In several studies of short-term rentals, spatial regression models together with various methods of spatial analysis are used. Indices measuring spatial autocorrelation are commonly used, while different spatial regression models, among them geographically weighted regression (GWR), are constructed [21,26,28,29,34,44,45]. When spatial regression is to be employed, it is typically preceded by an OLS regression in order to examine the independence of residuals.…”