Visualizing Photography Auction Results


Visualization

Concept and Dataset Procurement

 

Screen Shot 2015-03-11 at 16.49.20

How the results of the photography auction appear on Dreweatts’ website

I am interested in coming up with new ways large data sets can be brought to life.  As an archivist and a student of library science and art history, I am passionate about the history of photography. I want to investigate the circumstances that have led to the establishment of photography as an “accepted high art form” and that conception’s subsequent influence on the sales of photography at auction.

In order to analyze photography collecting trends, I have used python to scrape the results of six Dreweatts & Bloomsbury auctions that exclusively sold photographs. The availability of this information online made this project possible. Otherwise I would have had to of cross referenced  the results of the auctions with the catalogues to compile this dataset.

eg2

The JSON file that results from using python to scrape the auction results off of the website

Through a series of python scripts I was able to gather information about 1,415 photographs that were sold between 2010 and 2014. You can see the entire programming element of the project on github. The dataset for this information visualization project is comprised of: the name of the artist, the year in which the photograph was created, and the price for which the photograph sold or whether it did not sell. I then adjusted the prices for inflation, and since Dreweatts’ prices are in Pounds, I converted the amount to Dollars and adjusted for date specific conversion rates. In order to work at a more manageable level of trend analysis I used the date the photograph was created to generate the decade in which the photograph was created.

 

Visualizations

 

I wanted to visualize this data set to answer a few specific questions. What decade was the most prevalent at auction? Which decade generated the most revenue? Did prices at which the photographs sold increase from 2010 to 2014? Did famous photographers works’ sell at higher prices than less prominent photographers? The results of this study are not able to be extrapolated to industry wide trends since the dataset is by no means large enough to be indicative. However, this project is one step towards understanding trends in the photography market.

Using Tableau software, I imported my dataset and created visualizations to aid in understanding photography auction trends. In order to judge the results of the auctions, I first needed to look at the amount of photographs that were available by decade in each auction year. The following graph shows this distribution.

Screen Shot 2015-03-11 at 14.48.52From this graph we can see there were relatively few photographs created during the 1800s up for auction.  This makes sense because photography was only invented around 1820. Therefore the medium was not as prevalent, producing few images at a great cost. One of my assumptions was that the photographs up for auction during this time would sell for a large sum of money since they are scarce in comparison to the proliferation of images in the past century. Later on we will see how wrong I was. Turning back to the graph, we can see the sharp increase in photographs available from the 1950s onwards.  This was the result of many technical developments in photography: faster film speeds, the advent of color and flash photography, more affordable and user-friendly cameras, etc. You can view the interactive graph, where you can see the exact number of photographs available by auction year, by following this link.

The next visualization shows us the total amount of revenue generated by the sales of photographs broken down into the decade in which the photographs were created.

Screen Shot 2015-03-11 at 17.33.05

We can see that photographs created in the 1950s and 1960s generated the most revenue. This is not surprising as there were more photographs from these decades available for auction than any others. I was once again surprised. My assumption that older, rarer photographs would sell for the highest amounts. You can view the interactive version, where you can hover over the boxes to see the revenue generated by decade, by following this link.

(The following visualizations are too large to provide a screenshot. To view them, please follow the links.)

In order to answer my next question of whether prices at which the photographs sold increased from 2010 to 2014, I created a graph that shows the number and average price sold of photographs available by decade and auction year. To view the interactive version, follow this link.

In this graph we can see a very slight trend. Prices increased from 2011 to 2012, then decreased a little in 2013, and bounced back in 2014, but not to the levels of 2012. This trend can be seen in photographs from the 1860s, 1870s, and the 1940s through the 1980s. I do not know of any possible explanations for why this trend occurred. I wish I could have gathered more datasets from other auction houses and many more years, but the data was available without manually cross-referencing each sale with its catalogue entry.

The last visualization I created shows by artist the amount photographs sold for broken down by decade in which the work was created. You can select the price ranges to see whose photographs sold for the most amount of money.  You can also see whose photographs did not sell.  To use the interactive version of this visualization, follow this link.

I was surprised to find that both famous and less well known photographers’ works went unsold.  I was not surprised to see that Brassai and Dora Maar photographs’ were among the highest prices fetched.
The visualizations of the data created completely crushed any pre-conceived notions I had going into this project. I look forward to creating more visualizations with this data and garnering greater insight into the world of photography auction results.
J.E. Molly Seegers

Related posts:

  1. Percentage of Individuals Using Internet Around the World: 2000-2013
  2. Lung & Bronchus Cancer and Air Quality in the U.S. from 2010 to 2014 (A Tableau Public Dashboard)
  3. Disease Maps of the 1800s to Now!