This study assessed the economic value of public urban green spaces (UGSs) in Kuala Lumpur (KL) city by using the hedonic price method (HPM). It involves 1269 house units from eight sub-districts in KL city. Based on the hedonic price method, this study formulates a global and local model. The global model and local model are analyzed using ordinary least square (OLS) regression and geographically weighted regression (GWR). By using the hedonic price method, the house price serves as a proxy for public urban green spaces' economic value. The house price is regressed against the set of three variables which are structural characteristics, neighborhood attributes, and environmental attributes. Measurements of interest in this study are environmental characteristics, including distance to public UGSs and size of public UGSs. The results of the OLS regression illustrated that Taman Rimba Kiara and Taman Tasik Titiwangsa provide the maximum economic value. On average, reducing the distance of the house location to Taman Rimba Kiara by 10 m increased the house price by RM1700. Similarly, increasing the size of the Taman Tasik Titiwangsa by 1000 m2 increases the house price by RM60,000. The advantage of the GWR result is the economic value of public UGSs which can be analyzed by the specific location according to sub-district. From this study, the GWR result exposed that the economic values of Taman Rimba Bukit Kiara and Taman Tasik Titiwangsa were not significant in each of the sub-district within KL city. Taman Rimba Bukit Kiara was negatively significant at all sub-districts except Setapak and certain house locations located at the sub-district of KL. In contrast, Taman Tasik Titiwangsa was positively significant at all sub-districts except certain house locations at the sub-districts of Batu, KL, Setapak, and KL city center. In conclusion, results show that the house price is influenced by the environmental attribute. However, even though both of these public UGSs generate the highest economic value based on distance and size, its significant values with an expected sign are only obtained based on the specific house location as verified by the local model. In terms of model comparison, the local model was better compared with the global model.