The Economics of Blockchain-Based Virtual Worlds: A Hedonic Regression Model for Virtual Land

Abstract

Virtual worlds have become increasingly more important, and are being used for a large variety of recreational and business activities. Various companies have opened virtual offices and brand ambassador spaces, and the recent surge in online interactions has further increased the interest in these social platforms. In this paper, we analyze the first large-scale virtual world that has been built on a public blockchain network. We compile a comprehensive data set that allows us to study the economics of blockchain-based virtual property. We then use this data set to estimate hedonic regression models for virtual land and employ a semi-parametric Mixed Geographically Weighted Regression (MGWR) model that allows for spatially flexible coefficients. In particular, we expect the central business district (CBD) gradient and the street distance coefficients to vary across the virtual world. Conversely, we consider the coefficients of binary variables to be more meaningful in a global context. For inference purposes, we employ spatial robust heteroskedasticity and autocorrelation consistent (HAC) standard errors. We find evidence that prices of virtual land parcels are primarily driven by factors that are of particular importance in business applications. Land parcels in close proximity to popular landmarks, such as the city center, main streets, plazas and commercially-driven districts, as well as parcels with easily memorable address-like features were claimed at substantially higher prices. We used different model specifications and conducted various robustness checks, including a separate estimation for price segments and investor-specific effects. All specifications have led to similar results and support the evidence from our MGWR model.