Online housing search: A gravity model approach

In this paper we apply a gravity framework to user-generated data of a large online housing market platform. We show that gravity describes the patterns of inflow and outflow of hits (mouse clicks, etc.) from one municipality to another, where the municipality of the user defines the origin and the municipality of the property that is viewed defines the destination. By distinguishing serious searchers from recreational searchers we demonstrate that the gravity framework describes geographic search patterns of both types of users. The results indicate that recreational search is centered more around the user’s location than serious search. However, this finding is driven entirely by differences in border effects as there is no difference in the distance effect. By demonstrating that geographic search patterns of both serious and recreational searchers are explained by their physical locations, we present clear evidence that physical location is an important determinant of economic behavior in the virtual realm too.

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Are US Multinationals Making Rational Offshoring Decisions? A Gravity Model Approach

SSRN Electronic Journal ◽

10.2139/ssrn.975891 ◽

2007 ◽

Cited By ~ 1

Author(s):

Vrinda Gupta

Keyword(s):

Gravity Model ◽

Model Approach

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Online rental housing market representation and the digital reproduction of urban inequality

Environment and Planning A Economy and Space ◽

10.1177/0308518x19869678 ◽

2019 ◽

Vol 52 (2) ◽

pp. 449-468 ◽

Cited By ~ 6

Author(s):

Geoff Boeing

Keyword(s):

Housing Market ◽

Smart Cities ◽

Rental Housing ◽

Search Costs ◽

Poverty Rate ◽

The Internet ◽

Urban Inequality ◽

The Usa ◽

Housing Search ◽

Rental Housing Market

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.

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