The rising demand for rental spaces, such as hotel lodging rooms, has led property owners in high-potential areas to begin renting out their properties to meet this need. The Directorate General of State Assets (DJKN) under the Ministry of Finance, which manages state-owned assets, oversees approximately 1,100 room units with potential for rental. In alignment on increasing non-tax state revenue from state-owned assets (BMN), offering lodging room rentals as a form of BMN utilization presents a promising opportunity for expansion. To facilitate efficient rental services for these BMN rooms, a quick and accurate valuation process is essential. However, establishing fair rental prices for both property owners and tenants poses a significant challenge. This study, therefore, aims to develop automated valuation model (AVM). Since rental price predictions depend on various bebas variabels, the study employs several machine learning algorithms, including Ridge, Linear Regression, Random Forest (RF), Lasso, SVR, Elastic Net, and Extreme Gradient Boosting. The research focuses on rental markets in Jakarta, Bogor, and Bandung, using a dataset of fewer than 500 rental room samples sourced from marketplaces. These data points are adjusted for state-owned asset variabels, followed by feature engineering. The performance of the various algorithms is then compared. The results indicate that the best-performing model and the most important features vary by city, but overall, the Random Forest model delivers strong performance, with room size, road width, and room quality emerging as the most influential factors.
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