There has been relatively rapid growth in Somalia's real estate sector recently, resulting in property market booms in major cities of the country. Despite this sector's importance, very little scientific research has been conducted. Thus, this study aims to model the rental value of residential apartments in Mogadishu, Somalia. A hybrid modeling approach is utilized, in which a hedonic regression model was used in the first phase and an Artificial Neural Network (ANN) in the second. After analysis, the study established that an apartment's age, size, value, view, number of toilets, air quality, proximity to CBD, proximity to a university and probability of explosion are key factors determining apartment rental rates in Mogadishu, while floor level, parking space, and school proximity do not have a significant impact on apartment rental rates. Because of speculation, there is a higher likelihood of overvaluation than undervaluation in the housing market, which has profound policy and practical implications. Rather than being driven by real demand, Mogadishu's real estate market is driven by speculation, as overvaluation signals are more evident than undervaluation signals. As one of the world's poorest nations, where 70% of the population lives below the international poverty line, and about 80% of jobs are in the informal sector, extreme speculative activities adversely affect Somalia's economic quality and the living conditions of its citizens. Somali citizens need government policies that facilitate the accessibility, affordability, and adequate availability of decent housing. The study recommends protecting Somalia's real estate sector to attract more investors and boost the country's post-conflict development initiatives. A vital contribution of the study is that it is the first to examine the rental value of residential apartments in Mogadishu systematically. This study contributes significantly to housing economics and real estate development literature.