Online housing search: A gravity model approach

Abstract

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.

Online housing search dataset: Information flows of real estate platform users

“…DOI: https://doi.org/10.17632/rtn8t47t6j.1 [2] URL: https://doi.org/10.17632/rtn8t47t6j.1 Related research article J. Steegmans, J. de Bruin, Online housing search: A gravity model approach, PLOS ONE 16(3): e0247712 (2021). DOI: 10.1371/journal.pone.0247712 [3] . …”

“…The information flow datasets, or hit flow datasets, described in this data article are based on user-generated data of Funda.nl , the largest online housing market website of the Netherlands. The Dutch Association of Realtors (NVM) created Funda as an online real estate platform in 2001 [3] . Since then, Funda has become one of the most innovative digital housing platforms in the world.…”

“… Notes : Account, users that have registered and created a user account; Mortgage, users that have done the online mortgage calculation; Telephone, users that have clicked the button for the real estate agent’s telephone number; Email service, users that have signed up for an email service of new listings within their preferences; Message, users that have contacted the real estate agent through an online form; Viewing, users that scheduled a viewing with the online tool; Buyer, users that registered themselves as the buyer of a property. See also: Steegmans and de Bruin [3 , p. 11]. …”

“…We provide these data as the intentions for using the platform range from serious house searching to fully recreational search. Flow datasets are provided for users that have registered and created a user account, users that have done the online mortgage calculation at least once, users that have clicked the button for the real estate agent’s telephone number, users that have signed up for an email service of new listings within their preferences, users that have contacted the real estate agent through an online form, users that scheduled a property viewing with the online tool, and users that registered themselves as the buyer of a property (although which property that is is not registered) [3] . Columns 2 to 8 of Table 1 show that the number of zero flows increases as the number of hits in the subsamples becomes smaller.…”

“…Separate flow datasets are also provided for devices and network domain. The devices are split into desktop (including laptops), tablet, and mobile [3] . Table 2 shows that, for the first six months of 2018, the users of the website most often use a computer (almost 50 percent), followed by mobile phones (about 30 percent), and tablets (over 20 percent).…”