New land stats

For a raw dump file of the land sales over the last month and some additional land stats, see my posting in announcments at:

/3/6f/55089/1.html

The attachment of raw land sales is attached to this message as well.

Whups... In my excitement I missed this thread. reposting here

Booming business in 16 m2 plots

select sim,amount,size,pm from simtotals order by pm;

|  Voti        |  42500 |   4240 |    10.0236 |
|  Umber       |  43999 |   4096 |    10.7419 |
|  Stanford    |  11000 |   1024 |    10.7422 |
|  Nestor      |  72525 |   6656 |    10.8962 |
|  Tan         |   5500 |    496 |    11.0887 |
|  Limantour   |  55000 |   4848 |    11.3449 |
|  Slosser     | 241064 |  21136 |    11.4054 |
|  Scafell     | 282116 |  24512 |    11.5093 |
|  Semang      |   5985 |    512 |    11.6895 |
|  Shipley     |  27119 |   2224 |    12.1938 |
|  Bretton     |  42000 |   3360 |       12.5 |
|  Enceladus   |  12998 |   1024 |    12.6934 |
|  Blue        |   3625 |    272 |    13.3272 |
|  Hyles       |  76992 |   5760 |    13.3667 |
|  Inari       |  14000 |   1040 |    13.4615 |
|  Palulop     |   7500 |    512 |    14.6484 |
|  Clementina  |   6720 |    448 |         15 |
|  Boardman    |  47120 |   2736 |    17.2222 |
|  Isabel      | 179989 |   9824 |    18.3214 |
|  Derwent     |    300 |     16 |      18.75 |
|  Da Boom     |  50000 |   2048 |    24.4141 |
|  Paikea      |   1600 |     16 |        100 |
|  Clyde       |  10000 |     16 |        625 |
+--------------+--------+--------+------------+


Lots of churn in these sims..

 select sim,amount,size,pm from simtotals order by size;

|  Chaska      | 136308 |  65504 |    2.08091 |
|  Mullett     | 195074 |  69632 |     2.8015 |
|  Dryas       | 113872 |  70656 |    1.61164 |
|  Mesede      | 246784 |  72720 |    3.39362 |
|  Fourmile    | 320771 |  76048 |    4.21801 |
|  Parce       | 384999 |  78944 |    4.87686 |
|  Kawishiwi   | 303477 |  82432 |    3.68154 |
|  Steiger     | 234552 |  82672 |    2.83714 |
|  Velda       | 387151 |  86352 |    4.48341 |
|  Sphinx      | 224991 |  86992 |    2.58634 |
|  Wall        | 465006 |  91216 |    5.09786 |
|  Pryeri      | 190664 | 104368 |    1.82684 |
+--------------+--------+--------+------------+



mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size=16;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|       64165 |      4800 |                 13.37 |
+-------------+-----------+-----------------------+
1 row in set (0.03 sec)


mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size>16 and size < 256;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|      157332 |     29088 |                  5.41 |
+-------------+-----------+-----------------------+
1 row in set (0.02 sec)


mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size>255 and size < 512;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|      554244 |    117856 |                  4.70 |
+-------------+-----------+-----------------------+
1 row in set (0.02 sec)


mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size=512;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|     5579318 |   1438720 |                  3.88 |
+-------------+-----------+-----------------------+
1 row in set (0.03 sec)

mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size>512 and size < 1024;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|     1309956 |    245728 |                  5.33 |
+-------------+-----------+-----------------------+
1 row in set (0.03 sec)


mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size=1024;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|     7409343 |   1414144 |                  5.24 |
+-------------+-----------+-----------------------+
1 row in set (0.03 sec)

mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size>1024 and size < 4096;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|    10597790 |   2189424 |                  4.84 |
+-------------+-----------+-----------------------+
1 row in set (0.03 sec)


mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size = 4096;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|     2140501 |    397312 |                  5.39 |
+-------------+-----------+-----------------------+
1 row in set (0.02 sec)

mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size = 8192;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|      439680 |     90112 |                  4.88 |
+-------------+-----------+-----------------------+
1 row in set (0.03 sec)

mysql> select sum(amount), sum(size), sum(amount)/sum(size) from simdata where size > 8192;
+-------------+-----------+-----------------------+
| sum(amount) | sum(size) | sum(amount)/sum(size) |
+-------------+-----------+-----------------------+
|     5940862 |   1425376 |                  4.17 |
+-------------+-----------+-----------------------+
1 row in set (0.02 sec)

Ask me what stat you'd like to see for this data and if I can compute it I'll post it here.

So, did everyone a get a chance to read this stuff?

Please make requests! This is really cool data.

we need a series of these same data to mean anything

there's a casual explanation for each of the splits you've posted online but I'm sure you'd agree that a series of data would begin to show causal relationships more clearly

as i said in the other thread these are great data and you are right to highlight them

please save your queries in mysql so that they can be run against more data, if we get them

Well, some data is obvious to those of us who've been around.

For example, Da Boom and Board fetch high prices - there's really strong and clear reasons for this, and unless you've been paying attention you wouldn't know it. However, I didn't know about Isabel. But now I do.

The 16 metre plot price was something I also knew about, but didn't realise exactly how profitable it really was.

Finally, the churn on some of those plots are really wild. I'd like to know what's going on there, if anyone has any theories.

I thought the stratification you did was interesting but like you said the descriptive part about each sim is not common knowledge among non-vets. Would your comments refer to olders sims on the mainland with +/- 40 m land edit capabilities?

Also, I'm aware from my own First Land and land ventures of the aggressiveness of the 16 m2 market. Profits aside, at least anectdotallly it would seem that tactics and loose land edit skills drive most of that activity.

Churn is most likely just that: churn in the secondary market from speculation, or disgust from the lack of local zoning by otherwise happy residents.

Bump cause i think this is important

Well I think the $L falling is the biggest reason for churn. With a falling $L, the land market on average should get more expensive. The land prices are fixed to the US$ through unlimited supply auctions, so they work inversely to the $L market. So when the land prices rise, and land sellers don't adjust in time, people will most certainly jump in and grab their property to raise the price for them. In the near future the $L drops in value again, the new land seller does not adjust and someone else jumps in to raise the prices even more. Without a stable $L economy, you will always have churn.