Factors Influencing Rental Investments in Paphos, Cyprus: Comparing Short- and Long-Term Rental Strategies

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 touristic areas such as Paphos, Cyprus, where there is no clear distinction as to whether short- or long-term approaches are optimal. This 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 were collected from AirDNA and long-term data were 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 datasets, with the best-performing model for short-term properties being the Random Forest model (R-squared: 0.843), and the distance-based Multiple Linear Regression approach being the best for long-term properties (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 was found that locational characteristics did not impact the returns significantly when assuming that the property was located within a touristic area.