Many empirical studies assessing the economic benefits of urban green space have continually documented that green space tends to increase both value and sale price of nearby residential properties. Previous studies, however, have not fully captured the quality of neighborhood level landscape spatial patterns on housing prices. To fill this literature gap, this study examined the association between landscape spatial patterns of urban green spaces and single-family home sale transactions using a spatial regression model. The research was conducted through the analysis of 11,326 housing transaction records from 2010 to 2012 in Austin, TX, USA. Variables measuring the structural, locational and neighborhood characteristics of housing were coupled with Geographic Information Systems, remote sensing and FRAGSTATS to calculate several landscape indices measuring the quality of existing landscape spatial patterns. After controlling for any spatial autocorrelation effects, we found that that larger tree and urban forest areas surrounding single-family homes positively contributed to property values, while more fragmented, isolated and irregularly shaped landscape spatial patterns resulted in the inverse. The results of this research increase awareness of the role of urban green spaces while informing community design/planning practices about the linkages between landscape spatial structure and economic benefits.