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In the digital economy, innovation processes increasingly rely on highly specialised know-how and open-source software shared on digital platforms on collaborative programming. The information that feeds into the content on these platforms is provided voluntarily by a vast crowd of knowledgeable users from all over the world. In contributing to the platforms, users invest their time and share knowledge with strangers to add to the rising body of digital knowledge.This requires an open mindset and trust. In this study, we argue that such a mindset is not just an individual asset, but determined by the local communities the users are embedded in. We, therefore, hypothesise that places with higher levels of trust should contribute more to StackOverflow, the world’s largest question-and-answer platform for programming questions. In relating the city-level contributions of 266 OECD metropolitan areas to infrastructure, economic, and trust measures, we find this hypothesis confirmed. In contrast, click rates to the platform are solely driven by infrastructure and economic variables, but not by trust. These findings highlight the importance of societal values in the 21st century knowledge economy: if policy-makers want to develop a lively local digital economy, it is not enough to provide fast Internet access and business opportunities. Instead, it is equally important to establish a trust-building environment that fosters sharing of innovative ideas, collaborations, and knowledge spillovers.

As the rental housing market moves online, the internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings’ representativeness could impact different communities’ access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners’ and researchers’ ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the USA and finds that they spatially concentrate and overrepresent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities’ online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real time, it does not evenly represent all market segments. The internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies, human interactions, and planners’ ability to monitor and respond to urban challenges.

Although the recent housing crash put the U.S. economy into a tailspin, we know very little about how the massive increase and decrease of house prices influenced property tax collections. Using local level housing data from Zillow matched to property tax data from 1998 to 2012, two questions are examined. First, the elasticity of property tax revenue with respect to house values is estimated. Second, the timing of this elasticity is determined. The analysis is able to rule out that local policy makers capture the entire increase of house value in property tax revenues but unable to rule out that increases in house values are completely offset by changes in effective property tax rates. Decreases in house values have an elasticity of between 0.3 and 0.4 and take three years for changes in house values to impact property tax revenues. While property tax collections declined, local policy makers adjusted effective millage rates such that revenues did not decline as much as home values.