Study of spatial autocorrelation in the commercial real estate market of the city of Madrid
DOI:
https://doi.org/10.17561/ree.n2.2023.7854Keywords:
Gross yield, commercial premises, spatial autocorrelation, Moran Index, LISAAbstract
The availability of spatial attributes makes it possible to quantify the degree of clustering or randomness of a variable in space, thus offering valuable contributions to exploratory analysis. Particularly, the use of spatial autocorrelation indices makes it is possible to delve into the distribution of a variable at both global (Moran's index) and local (LISA) scales and identify patterns between spatial units whose neighborhood links are set according to the hypothesis.
However, despite the extensive use of such indices in fields such as ecology and epidemiology, the use of these techniques in the analysis of variables in real estate contexts has been limited, although it should be noted that it helps to define the importance of the spatial component in the value of a property and whether there is a clear relationship with surrounding properties.
In the context of the national real estate market, and given the importance of retail trade, specifically in Madrid, the use of such indices is essential. Therefore, this paper offers an exploratory spatial analysis corresponding to data on commercial premises for sale and rent in 2020, a period characterized by the compulsory closure of non-essential trade and subsequent limitations in capacity, opening, and closing hours.
The results show patterns among the 21 districts that make up the city, serving as a basis for the study of the gross yields of commercial premises, which in turn have been among the real estate products with the most favorable evolution in the last year.
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Copyright (c) 2023 Romina Andrea Silvera Vies, Ana Debón, Javier Ribal
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