Evaluation of spatial data’s impact in mid-term room rent price through application of spatial econometrics and machine learning. Case study: Lisbon

Abstract

Household preferences is a topic whose relevance can be found to dominate the applied economics, but whereas urban economies view cities as production centers, this thesis aims to give importance to the role of consumption. Provision to PoIs might give explanation to what individuals value as an important asset for improvement of their quality of life in a chosen city. As such, understanding short-term rentals and real estate prices have induced various research to seek proof of impacting factors, but analysis of mid-term rent has faced the challenge of being an overlooked category. This thesis consists of an integrated three-steps approach to analyze spatial data’s impact over the mid-term room rent, choosing Lisbon as its case study. The proposed methodology constitutes use of traditional spatial econometric models and SVR, encompassing a large set of proxies for amenities that might be recognized to hold a possible impact over rent prices. The analytical frameworks’ first step is to create a suitable HPM model that captures the data well, so significant variables can be detected and analyzed as a discrete dataset. The second step applies subsets of the dataset in the creation of SVR models, in hopes of identifying the SVs influencing price variances. Finally, SOM clusters are chosen to address whether more natural order of data division exists. Results confirm the impact of proximity to various categories of amenities, but the enrichment of models with the proposed proxies of spatial data failed to corroborate attainment of model with a higher accuracy. (Nüst et al., 2018) provides a self-assessment of the reproducibility of research, and according to the criteria given, this dissertation is evaluated as: 0, 2, 1, 2, 2 (input data, preprocessing, methods, computational environment, results).