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Craigslist Listing Growth Rates

May 2, 2007

A few days ago, I posted Craigslist listing data for selected cities. To take the analysis a little further, today I looked at the data that best fit exponential growth and calculated doubling periods for each site (doubling periods are easier to think about than exponential growth rates).

Here are the doubling periods in days and the DMA sizes associated with each Craigslist site. DMA numbers are the number of households in the area. DMA Housholds Doubling Time (Days)
boise 239,376 111
denver 1,550,960 322
miami 1,515,347 356
lasvegas 635,356 318
memphis 668,804 236
westpalmbeach 707,934 188
indianapolis 1,029,361 231
fortmyers 440,261 169
orlando 1,287,863 190
houston 1,859,586 215
dallas 2,201,625 203
nashville 922,435 176
sanantonio 753,076 165
tampa 1,663,780 188
jacksonville 614,068 169
Average   225

Below, the data are shown as a scatter plot. There doesn’t seem to be much correlation between DMA size and growth rate. In fact, the fastest growing Craigslist site also has one of lowest number of (DMA) households in the data set. On the Boise, ID, Craigslist site, the number of live listings per day in the for sale, services, jobs and housing is doubling every 110 days.

Craigslist doubling period vs. DMA size

This histogram shows the distribution of growth rates for the data set. The average doubling time for the number of live listings per day on Craigslist is 225 days for this data set.

Craigslist doubling periods

Some Caveats:

  • Ad duration: it appears that Craiglist may adjust the ad duration and/or scrub ads periodically. It may be that they control the number of live ads for a new site by increasing the ad during when sites are young and then cutting it down later to ensure fresher content. This means that Craigslist may target a specific growth rate.
  • Categories: The categories of for sale, services, jobs and housing were chosen to match traditional newspaper categories. The volume of traffic and content in other Craigslist categories may attract online visitors and effect the overall growth rate of the listings counted in this data set.
  • Life cycle: I don’t have data showing the start date for various sites so this data glosses over any differences in the life cycle of a new Craigslist site and how that may be affected by DMA size.
  • “Fits exponential Growth” means that I performed linear regressions on Ln(lisings) vs time and chose all the sites with a correlation coefficient > 0.9.
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