Recent commitments by the Biden administration have set forth aggressive targets towards achieving a net-zero energy system by 2050. These policies are expected to spur a rapid transition to clean energy technologies and a commensurate need to develop and deploy energy storage technologies. Recently there has been a renewed awareness that pumped storage hydropower (PSH) is an attractive solution as it is commercially mature and can play a critical role in balancing intermittent renewable energy sources, providing essential grid services. PSH can provide grid flexibility and stability by enabling the dispatching of disparate variable renewable energy technologies. Despite its promise, PSH is often overlooked in the grid planning efforts including integrated resource and capacity expansion planning by utilities, independent system operators, and other analysts. One reason PSH is often overlooked is its highly uncertain development costs. Such uncertainty exists because of site-specific technical concerns and challenges with licensing, permitting, and financing large new capital projects. There is even greater cost uncertainty in the public domain, as most publicly available PSH construction cost information is outdated and does not reflect the current market and policy conditions in the hydropower industry. To address this major gap in PSH cost information, we have developed a generalized model, in collaboration with industry experts, that provides improved cost estimates of the construction and installation of closed-loop PSH in the US. The data that underpin the model reflect a broad range of closed loop PSH site specifications and geographic locations in the conterminous US. All costing data have been vetted by independent contractors with expertise in a range of PSH projects and reflect the current state of the industry both in terms of equipment, specifications, and costs. The model is parameterized, scalable, and developed to be used by a wide range of stakeholders. It is developed using an open-source framework and will be released publicly by the end of the 2022 calendar year. We expect to maintain and continue to update the model as new data are made available as well as in collaboration with the user base. We will present results from this work along with examples.