Marktplaats will not tell you how many people get scammed on a daily basis, but me, a bored programmer can. By creating a program that monitors listings, we can track which accounts got banned.
The Python script marktwatch does the following:
- Visit the nvidia
960/970/980/1060/1070/1080graphics card listings 3 times a day
price/description/seller/location/dateand more in a database
- By visiting previously saved user profiles, we can see if they still exist. If they do not, they are probably banned.
- If they got banned, check what listings they had and for what price
- Only examine private listings (excluding webshops)
If we let this program run for X amount of time, we can analyse the database and conclude:
- The ratio of sellers to scammers
- The ratio legit listings to scams
- Make an educated guess as to how many people got scammed and how much they lost
Operation boredom GO
I was able to collect data from and around the month of August.
SELECT COUNT(*) FROM listing; SELECT COUNT(DISTINCT seller) FROM listing; SELECT COUNT(DISTINCT seller) FROM listing WHERE removed; SELECT COUNT(*) FROM listing WHERE removed;
- 978 listings in total
- 582 unique accounts
- 114 removed accounts (19%!)
- 143 removed listings (14%!)
19% of all user accounts that I came across in a months time were deleted after a short time. Let’s continue what this actually implies for the bad guys:
SELECT AVG(price) from listing where removed;
Listings from removed accounts had an average of 330€.
Tin-foil hat time
Let’s try to find out how much money the bad guys make.
- Marktplaats starts terminating user accounts after multiple reports, e.g., 3 reports.
- Within this timeframe, at least 2-3 people can get scammed but could be a lot more - lets just go with 2 to be on the safe side.
Our ‘equation’ *kugh* becomes:
(avg(scam price) * scammers) * 2
SELECT (SELECT AVG(price) FROM listing WHERE removed) * (SELECT Count(*) FROM listing WHERE removed) * 2;
Which brings us to 94.478€ of scam profits over 143 listings (out of 978).
As previously mentioned, this is just a raw educated guess - it’s up to the reader to verify my conclusions or perhaps do some research him/herself.
Most popular models for scams
SELECT COUNT(id), make FROM listing WHERE removed GROUP BY make ORDER BY COUNT(id) DESC;
The illegitimate listings targeted the more expensive cards the most:
Avarage account age for scams
SELECT seller, active_since_days from listing WHERE removed GROUP BY seller ORDER BY active_since_days;
SQL Output: link
- 50% of the accounts are less than a year old
- 37% of the accounts are less than a week old
There are still some old accounts to be spotted within this list. Older accounts seem more legit and as such you would pay more to obtain one. I suspect hacked accounts can be bought at some shady darkweb forum.
SELECT count(*), location FROM listing WHERE removed GROUP BY seller ORDER BY count(id) DESC;
SQL Output: link
Looking at this output, we can see most of these graphics cards are generally located in the less populated areas of The Netherlands. Buyers are reluctant to travel far to pick up a card so they pay via bank transfer.
Sorted per province:
SELECT *, COUNT(1) FROM (SELECT SUBSTR(location, INSTR(location, ',') + 2, 5) FROM listing WHERE removed GROUP BY seller) GROUP BY 1 ORDER BY 2 DESC;
For some odd reason, Flevoland is not included in the list.
10-15% of the video card listings are scams. Draw your own conclusion!