This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.

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Past research has demonstrated the racially and spatially uneven impacts of economic shocks and environmental disasters on various markets. In this article, we examine if and how the first few months of the COVID-19 pandemic affected the market for rental housing in the 49 largest metropolitan areas in the United States. Using a unique data set of new rental listings gathered from Craigslist and localized measures of the pandemic’s severity we find that, from mid-March to early June, local spread of COVID-19 is followed by reduced median and mean rent. However, this trend is driven by dropping rents for listings in Black, Latino, and diverse neighborhoods. Listings in majority White neighborhoods experience rent increases during this time. Our analyses make multiple contributions. First, we add to the burgeoning literature examining the rental market as a key site of perpetuating sociospatial inequality. Second, we demonstrate the utility of data gathered online for analyzing housing. And third, by reflecting on research that shows how past crises have increased sociospatial inequality and up-to-date work showing the racially and spatially unequal effects of the COVID-19 pandemic, we discuss some possible mechanisms by which the pandemic may be affecting the market for rental housing as well as implications for long-term trends.

As more urban residents find their housing through online search tools, recent research has theorized the potential for online information to transform and equalize the housing search process. Yet, very little is known about what rental housing information is available online. Using a corpus of millions of geocoded Craigslist advertisements for rental housing across the 50 largest metropolitan statistical areas in the United States merged with census tract–level data from the American Community Survey, we identify and describe the types of information commonly included in listings across different types of neighborhoods. We find that in the online housing market, renters are exposed to fundamentally different types of information depending on the ethnoracial and socioeconomic makeup of the neighborhoods where they are searching.

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.