Version 1
: Received: 1 April 2024 / Approved: 2 April 2024 / Online: 2 April 2024 (13:03:12 CEST)
Version 2
: Received: 8 April 2024 / Approved: 9 April 2024 / Online: 9 April 2024 (11:52:38 CEST)
How to cite:
Martin, S.; Dimopoulos, T.; Katafygiotou, M. Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies. Preprints2024, 2024040187. https://doi.org/10.20944/preprints202404.0187.v2
Martin, S.; Dimopoulos, T.; Katafygiotou, M. Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies. Preprints 2024, 2024040187. https://doi.org/10.20944/preprints202404.0187.v2
Martin, S.; Dimopoulos, T.; Katafygiotou, M. Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies. Preprints2024, 2024040187. https://doi.org/10.20944/preprints202404.0187.v2
APA Style
Martin, S., Dimopoulos, T., & Katafygiotou, M. (2024). Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies. Preprints. https://doi.org/10.20944/preprints202404.0187.v2
Chicago/Turabian Style
Martin, S., Thomas Dimopoulos and Martha Katafygiotou. 2024 "Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short and Long-Term Rental Strategies" Preprints. https://doi.org/10.20944/preprints202404.0187.v2
Abstract
Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.
Keywords
AirDNA; Airbnb; Random Forest; K-Nearest Neighbour; Multiple Linear Regression; Geographic Information System
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
