Determinants of single-family residential property values in Ethiopia: a comparative analysis of willingness to pay or receive and real transaction data

Purpose This study aims to identify determinants of single-family residential property values and fill the gap by analyzing respondents’ willingness to pay/receive data alongside real transaction data. Ordinal logistic regression and ordinal least square regression were used. Design/methodology/approach Ordinal logistic regression effectively analyzes willingness-to-pay/receive data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantifies the impact of continuous and categorical predictors on real transaction data. Findings Findings revealed strong associations between property values and several variables. Analysis of willingness-to-pay/accept data from 232 respondents showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district ( p < 0.05). Similarly, ordinal least square regression analysis of transaction data confirmed the significance of most of these factors, except for property orientation, which indicates the difference of preference in the local market or reporting inconsistencies, demand further investigation. Variables such as views, proximity to wetlands, roads, green areas, religious institutions and schools were statistically insignificant across both data sets ( p > 0.05). Practical implications It provides a robust basis for housing and urban development strategies. The stakeholders such as real estate developers, urban planners and policymakers are encouraged to incorporate these findings into housing policies, land value capture initiatives and urban planning frameworks to enhance residential property value and align with sustainable urban development goals. Originality/value This study contributes original insights into single-family residential property valuation by integrating willingness-to-pay and transaction data, substantiating the determinants of property value.