The ether_real_estate Package

This software simulates a game in which players purchase properities that rent out real estate to businesses. When leases expire, players submit bids to the firm for a new lease, and the winning player adds the firm to one of their properties for the duration of the lease.

All transactions take place on a private Ethereum blockchain, which allows students to explore blockchain technology as a ledger for recording transactions.

Loading the Game

The key blockchain component of the game utilizes the eth-tester module. The module makes use of the pyEVM backend, which is currently listed in an experimental (alpha) stage. The eth-tester module is Linux-only for now. There are ways to run the game on Windows by interfacing with something like Ganache in the Truffle Suite for Ethereum development, and a later version of ether_real_estate will enable this functionality.

Install the following packages from PyPI. Because ether_real_estate and some of its core dependencies (eth-hash, eth-tester, py-evm) are in early development, the ether_real_estate module is picky about version numbering for dependencies. Create a virtual environment if you want to use other versions of the dependency packages for other projects that will not make use of ether_real_estate.

!pip install ether_real_estate
!pip install eth-hash[pycryptodome]

Requirement already satisfied: ether_real_estate in /mnt/software/anaconda3/lib/python3.8/site-packages (1.1)
Requirement already satisfied: eth-hash==0.3.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from ether_real_estate) (0.3.1)
Requirement already satisfied: pandas in /mnt/software/anaconda3/lib/python3.8/site-packages (from ether_real_estate) (1.2.2)
Requirement already satisfied: numpy in /mnt/software/anaconda3/lib/python3.8/site-packages (from ether_real_estate) (1.19.2)
Requirement already satisfied: py-evm==0.4.0a4 in /mnt/software/anaconda3/lib/python3.8/site-packages (from ether_real_estate) (0.4.0a4)
Requirement already satisfied: eth-tester==0.5.0b4 in /mnt/software/anaconda3/lib/python3.8/site-packages (from ether_real_estate) (0.5.0b4)
Requirement already satisfied: eth-utils in /mnt/software/anaconda3/lib/python3.8/site-packages (from ether_real_estate) (1.10.0)
Requirement already satisfied: eth-keys<0.4.0,>=0.2.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-tester==0.5.0b4->ether_real_estate) (0.3.3)
Requirement already satisfied: rlp<3,>=1.1.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-tester==0.5.0b4->ether_real_estate) (2.0.1)
Requirement already satisfied: semantic-version<3.0.0,>=2.6.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-tester==0.5.0b4->ether_real_estate) (2.8.5)
Requirement already satisfied: eth-abi<3.0.0,>=2.0.0b4 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-tester==0.5.0b4->ether_real_estate) (2.1.1)
Requirement already satisfied: cached-property<2,>=1.5.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (1.5.2)
Requirement already satisfied: lru-dict>=1.1.6 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (1.1.7)
Requirement already satisfied: mypy-extensions<1.0.0,>=0.4.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (0.4.3)
Requirement already satisfied: pyethash<1.0.0,>=0.1.27 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (0.1.27)
Requirement already satisfied: eth-bloom<2.0.0,>=1.0.3 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (1.0.4)
Requirement already satisfied: trie==2.0.0-alpha.5 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (2.0.0a5)
Requirement already satisfied: py-ecc<5.0.0,>=1.4.7 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (4.1.0)
Requirement already satisfied: blake2b-py<0.2,>=0.1.4 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (0.1.4)
Requirement already satisfied: eth-typing<3.0.0,>=2.2.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from py-evm==0.4.0a4->ether_real_estate) (2.2.2)
Requirement already satisfied: typing-extensions<4,>=3.7.4 in /mnt/software/anaconda3/lib/python3.8/site-packages (from trie==2.0.0-alpha.5->py-evm==0.4.0a4->ether_real_estate) (3.7.4.3)
Requirement already satisfied: sortedcontainers<3,>=2.1.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from trie==2.0.0-alpha.5->py-evm==0.4.0a4->ether_real_estate) (2.3.0)
Requirement already satisfied: hexbytes<0.3.0,>=0.2.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from trie==2.0.0-alpha.5->py-evm==0.4.0a4->ether_real_estate) (0.2.1)
Requirement already satisfied: parsimonious<0.9.0,>=0.8.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-abi<3.0.0,>=2.0.0b4->eth-tester==0.5.0b4->ether_real_estate) (0.8.1)
Requirement already satisfied: pycryptodome<4,>=3.6.6 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-hash==0.3.1->ether_real_estate) (3.10.1)
Requirement already satisfied: cytoolz<1.0.0,>=0.10.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-utils->ether_real_estate) (0.11.0)
Requirement already satisfied: toolz>=0.8.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from cytoolz<1.0.0,>=0.10.1->eth-utils->ether_real_estate) (0.11.1)
Requirement already satisfied: six>=1.9.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from parsimonious<0.9.0,>=0.8.0->eth-abi<3.0.0,>=2.0.0b4->eth-tester==0.5.0b4->ether_real_estate) (1.15.0)
Requirement already satisfied: python-dateutil>=2.7.3 in /mnt/software/anaconda3/lib/python3.8/site-packages (from pandas->ether_real_estate) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /mnt/software/anaconda3/lib/python3.8/site-packages (from pandas->ether_real_estate) (2021.1)
Requirement already satisfied: eth-hash[pycryptodome] in /mnt/software/anaconda3/lib/python3.8/site-packages (0.3.1)
Requirement already satisfied: eth-utils<2,>=1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-hash[pycryptodome]) (1.10.0)
Requirement already satisfied: pycryptodome<4,>=3.6.6 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-hash[pycryptodome]) (3.10.1)
Requirement already satisfied: eth-typing<3.0.0,>=2.2.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-utils<2,>=1->eth-hash[pycryptodome]) (2.2.2)
Requirement already satisfied: cytoolz<1.0.0,>=0.10.1 in /mnt/software/anaconda3/lib/python3.8/site-packages (from eth-utils<2,>=1->eth-hash[pycryptodome]) (0.11.0)
Requirement already satisfied: toolz>=0.8.0 in /mnt/software/anaconda3/lib/python3.8/site-packages (from cytoolz<1.0.0,>=0.10.1->eth-utils<2,>=1->eth-hash[pycryptodome]) (0.11.1)

Then load the module.

import ether_real_estate

A couple notes about how the game runs:

Transactions are handled by the game directly. In a later version, transactions will be auto-enforced via smart contracts in Vyper so that students can learn about that aspect of blockchain development.
Gas prices are currently set to zero to keep things simple. Variable gas prices will be added later to simulate transaction costs.

The game will save/read data from a user-specified directory.

Instantiating the Game

A new Game object, g, is created below. Options for the Game object are:

num_industry (default = 5)
- Number of industries in the game. Firm cash flows are partially correlated within an industry.
num_players (default = 5)
- Number of human players in the game. This should be set to either the number of students in the class or the number of groups in the class (if working in teams).
num_ai' (default = 0`)
- Number of AI players in the game.
- AI players can comprise the full user list if testing the game.
- Alternatively, AIs can be added to student games for small classes.
verbose (default = False)
- If True, print out extra information at each step of the game. Useful for testing.
game_path (default = '')
- Path to store game data
- If left blank, the user will be asked to provide one (or a default will be suggested).

Some notes about the economy:

There will be 3 times as many buildings are there are players.
There will be 20% more firms than can fit in all of the buildings.

The command below creates an all-AI game for testing purposes. I specify 5 industries, 0 players, 5 AI players, and a game folder test_game set under my home directory.

g = ether_real_estate.Game(num_industry=5, num_players=0, num_ai=5, verbose=True, game_path='/home/james/test_game')

Starting deposit transactions: ('0x25f5c23f5e31bb112d18d980c74912529bde17b83f7068581f6e354ecd08d46d', '0x6c4cfe34d1125d704be2ab60f0e70b152fcaf6a16f8854e8dbc654f48040106a', '0xcdb9b6c4378ce4b2aaf87200c538c68fa37b1a9d5dccd2941b63f3326bc63b72', '0xac8360365d3cb3eabdb7f837ed0f327fecf77d05b224c95ad6c28891cd67e075', '0x92a63e09d3d234a61c8eb27fb3ed58648a1ef146847e36741df451377fea6467', '0x0cd15ba5656ba3f5f33d0538393fc6109eef9bd1d5ce4e701165690d74f4740c', '0xb43b1840dba519f88c863df4a10d4aeab7d380224c0ac09a32eee9a6b009f256', '0xf65187fa5235546912423a7a00455098dd8a01bb9a077ef2e582760a13008d33', '0x262c75e2363d97b550005abb97a3c557bc5c475a388ec8e82ab28789d4ef4700', '0xe2d1d2f4c7903f26c81921ed036b6a46cd5ea081a5d5925e9dc3c424630f5fb4', '0xde9c89250fd7b0bc5924c1f351e750c3d65d3175e881f11c9f1475558086b66b', '0xaabb2eaf031d0031567104a9abd6e8550844b648a95aa926c1c5407e45b48c02', '0x2f33b9d18929154ecfaf7a69e5a803a5c7aa69717d3a932eba62d33165aaf652', '0xc29d2a21b00f98b6b604d93da11c4e0c8bb69265617b0940c1ef6a75c7b239c3', '0xd7ed552ed40e9a997a9632cb7b49aaf75a999e124c4b12edbc3e0051e5d91dd8', '0x4c205bff5765ddbc59795d310fd81cc2d9f75d0579b9c707d2e9eb6ffea4ca48', '0x78f50c87be4d6db9930a440c014e104ef677479403fc54823d3f81ee8d99b1f5', '0x6980ada7ed17b8bab16bbcd86455eee9c6389e692203d18ce6a84449c903d6b0', '0x09cf46bd135a442285c9b985cd9ee1346c0df72f4f4877daf725bb6faabfeb87', '0xe000f137e882230d799b5ce60adf2282dcf01eefcd789098359003f970abc5f6', '0x4a6358fbd8908a7f673cc72e137bb87783514ed220316bdcf25a9290ddd6dc59', '0x9855b92c06a61421a21a489e0789b57f49d682e70209ba7335d81814bcf76c32', '0x7c7e0a959f868805ed84c34bdbed681238802a94515549d9ed5d127671ada634', '0x988a3fdb86e243caaeb9688f9c2388efc97729631e0ff716c5eb48f7b861bc07', '0xd06bd53c8d07683bf29eeaa6b2badf358caaa3a7932c189eb9efc27232145fcc', '0x6b88028d7effba8b28d298586ed1574cbe0ef872f3e0f76c438be4cf260f4079', '0x8cb118131fa2652159a14adc23efed02b3432ce71a761db2be3082f70f324e93', '0xdb7726c42d05d6e2f9db493360abee1b8ba65820965ced8a919ba5e81b805217', '0x910957c1d4c70540e510d609e4ee7bfc525d2a88c2b185765a986a91eaf27d41', '0xaa5fe63420d5f072ba429cd34edfa0087c37a319978bfeed02f7a3d9cee0379b', '0xda8bedb870fb45bab97e0b9c90df2048bc8f7b8bf44a4d6700eb299f4ec98430', '0xe530f1cb0710b2c916a6c55be782071fd87be675f13c5e71a3d278dcc785a03d', '0x205f328fd56d2bc0fd488b4280ce757d92727e88745a68c875df7921f9e4cca1', '0x292e0a339c86ae9d0946aba8e64fa3af68caabbfeaf0dbc40303e5a85fc69990', '0x444fae63fda58789bf699db146be9fb3f2a22c9758a5618d6787e478a0abeb17', '0x55faaa90403ecb0acc7777d468566e9b805d0557c9ab7d92b0f561a395e86cee', '0xe3f5e24a18a86d24b0d5aad2cb27a6957116cb75aa365994f5b37c0983c80d0c', '0x35d380a5b1eb38fb8795c158539817b0f7b3b68bfc35f8385a500cd2986186e6', '0x9836a600327e0313ec5db443ea839f9d53c3273522f7b38be7e55220a12c0b0a', '0x0c90de936140a26a68eccb04befdd36755aa674ae0c7d25efc1749007678ae00', '0x9531712dcc4d4ac7edeb57c97f4d4e3dece942c88db9c108f02de3e0e686c58f', '0xb27d52e98f8309cb47899de91e3115ce6c8406f74f91841f3939245529a39bbb', '0xcaa74ab533d47096aa292b52e8a613e79a2f970a23b8ed4c85b071c3a985da63', '0xeb19c460d46439bb23b616bdc3f358399ad208cd0f6d6defafe43367041388af', '0xf0cfc1e505b2e07f85274656fdc4b30a5d99dbd51f2b37d3e28e11dfde3ce9ca', '0xf454172d7866b25897c0c96f068504fea34ddb101bbd133cab746968a04dbe1d', '0x41abc2b8146e2a0d3c2544ebf352d4f08d0289b77953e98e5754a2917f6e43b3', '0x1d8d63b312db1a9541c91db13fc4d83b87a8fda140a6acae6739a88586341dce', '0x0d8743f563e5a1f222c058098ae817855bfb498fdef305f71bdc2df85ccc3281', '0x9f5271a52017d79477821943ef29174de913709c04cf101bd68ee908705cebd3', '0x9cbd6352404a4753493180ab66c4a668d9080cc033113a38125930971a75d2c3', '0x2aba42d2e8df3b115b8e3ac217d4dded2a7ac6c5c3c081c3b0e2e17b111c1631', '0xc9a443c1388d4afea7aa92885c04df5a426b86fb84d3e305ad965cebbf130920', '0x4c9d8fb61925315e04a0e65659005b8c35ff54bc158f81265db018d1f79a0cf5', '0xf3c89e42f0ad7120c5b041e355a88a4afc34f29db2be92221597cd5103e502d9', 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'0x80f994c2d6dff5449183a81c5c131a9c83095f674c6fb1df0ced37411179254e', '0x77d09afe4409f3409ddf5cb73d9a517296a12fe9082e6098687873ebebc10a44', '0x228a141821b7ec9e9b5d886ad20c956a73013176a96ffe796a645590999d74b5', '0x19b561c87237d1c16a2dba6fab60cd314ba8e63842c2272b01ebd70af83259f8', '0xf9c4521d1d365d57bb9453c405872cedfeb8870730663deb8168c75203efb56a', '0xdc10913d4abc7c5cf656939bce9730ebf5e91dfaa7acf25b644bb7a9e105b146', '0xab0f0b28ff505043b726f71c74f4cfd330c51f060002cbc9ecaa2b640e160789', '0x2bed42efdb7135474c62013968e10a9cad47756e2d087c01bb5050d5d4591e16', '0x47e43957dad2a1487bafcd72249f5ebcc018e1361a685066d3f9b70aa8c57f8d', '0x89c2a3867153a14aa4ec997879f7016674dfce8484f62a6785932e4990e95561', '0x8797af59c867a65196ba7c3185554978decc61258ec224d26d8fb5f539c243fa', '0xa66b6c9ad8f219281d34ed508ae25c81450a6ebe1817d10c364c97e292a92946', '0xf04e5bbeba1059558ba62a2f1cb3565ba15c1ff632d7a5a0dd4aed744959de6e', '0x5ac7cdd4efa7645ed3ad8094c1db13d05936a35e7b165e4fed197ec53ccf14ca', 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Saving the game

All functionality of the ether_real_estate module works through the Game object.

When a Game object is created, there are five directories created under the game_path directory that is specified during instantiation. A brief summary of each subdiretory is listed here. They will be explored one-by-one deeper into the tutorial.

1. building_bids: This directory will receive human/AI player bids on unowned buildings. These buildings will have a portfolio of lease agreements as well as potentially some vacant units, and at each time period players can bid on unowned buildings. The Game object will select the winning bid.

2. firm_bids: This directory will receive human/AI player bids on firms not currently under a lease contract. At each time period, players can bid (suggest lease contracts) to firms not under contract. The Game object will select the winning bid, and the winning player assigns the firm to one of the player’s buildings.

3. stats: Summary data for the state of the economy is output each period to this directory. For each building, a list of which firms currently occupy that building are given. Firm industry membership and lease agreement (if available) are also provided.

4. cash_flows: Historical cash flow data for each firm is recorded here. Firm cash flows are given by: $y_{i,t} = \sum_{k=1}^6 \beta_{i,k} y_{i,t-K} + \epsilon_{i,t} + \nu_{j,t}$ where $\epsilon_{i,t}$ and $\nu_{j,t}$ are firm and industry-specific shocks. Players should use these historical cash flows to determine rental payments and default risk.

5. blocks: Each mined block of the Ethereum blockchain on which transactions occur is saved here for easy access by players. Because bidding for buildings and firms is a competitive process, players would do well to review the blockchain and determine the financial strength of their rivals.

Bidding

At the start of the game (period 0), no buildings are owned. Players must bid for them. Building data is stored in the stats directory. A snapshot of this data is shown below.

import pandas as pd

building_stats = pd.read_csv(g.path + '/stats/0/buildings.csv')
building_stats.head()

	building	size	firm	industry	firm_size	rent	maturity	cash_holdings
0	B0	20	F0	0	1	4	2	4.0
1	B0	20	F1	1	1	5	2	5.0
2	B0	20	F2	2	1	5	2	5.0
3	B0	20	F3	3	1	4	3	4.0
4	B0	20	F4	4	1	3	3	3.0

All buildings are given a size of 20. Firm sizes start at 1 and grow with cash holdings. Firm size increments to 2 when cash holdings exceed 5 at the end of the period. Firm size increments at each multiple of 5 thereafter.

It is possible for the size of all firms in a building to exceed building occupancy. In this case, the Game object randomly selects from the set of firms that increased in size in that period and removes them from the building until occupancy is within limits. This can create strategic value in reserving some spare occupancy in a building.

Human players create bids for buildings and submit these bids as a .csv file to the building_bids directory. Once all human players have submitted bids, the game organizer (e.g. instructor) can instruct the Game object to create AI bids and then select winning bids for each building.

g.get_building_bids()

	 Property acquisition completed:
	 player: p0 eth: 14.44375106499403 properties: ['B2', 'B7', 'B8']
	 player: p1 eth: 204.3377445424868 properties: ['B4', 'B6']
	 player: p2 eth: 354.48224739631064 properties: ['B5']
	 player: p3 eth: 153.5332989721354 properties: ['B3', 'B9']
	 player: p4 eth: 190.28382811566786 properties: ['B12', 'B13']

Here, the 5 AI players have bid on buildings and the winning bids were selected. Note that winning more or fewer buildings at period 0 does not necessarily set the player up for failure. Over many simulations of real estate games, I’ve found players with one building to win the game as well as players with three buildings to win the game. Success depends on building management (contract selection) over the following periods.

A snapshot of bids for player p0 is shown below.

p0_building_bids = pd.read_csv(g.path + '/building_bids/0/p0.csv', header=None)

p0_building_bids.head()

	0	1
0	B2	175.342916
1	B7	175.131356
2	B8	172.942416

Once buildings are awarded, players now have a knowledge of the amount of spare occupancy in their buildings. For players with spare space, bids for firms not currently under contract with a building can be bid for.

Human players submit their bids to the firm_bids directory. After all humans have submitted bids, the game organizer can instruct that Game object to create AI bids and then select winning bids for each firm.

Bids on firms are a rent-duration pair that specify the terms of the contract. The Game object will automatically determine the best contract to select a winning bid (based on the expected contract cost). Because some contracts will propose shorter durations than others, the Game object will make a naive guess at contract renewal rates in order to make short and long-term contracts more comparable.

g.get_firm_bids()

A snapshot of firm bids from player p0 is shown below.

p0_firm_bids = pd.read_csv(g.path + '/firm_bids/0/p0.csv', header=None)
p0_firm_bids.columns = ['firm name', 'rent', 'duration']

p0_firm_bids.head()

	firm name	rent	duration
0	F270	4	2
1	F271	5	3
2	F272	3	2
3	F273	3	3
4	F274	4	2

Players can bid on as many firms as they like (from the set of firms not under contract). The Game object randomly selects firms from the bid list and awards winners. As soon as a player has completely filled their building occupancy, additional firm bids from that player are discarded.

Saving the Game

The Game object can be saved so that it can be resumed at a later date. For example, if the game organizer is running the game on Google Colab then Python will time out after so many hours. Before the kernel times out, the organizer should save the game, which pickles a number of features about the game so that it can be resumed later.

g.save_game()

Saving the game

A saved game can be resumed by specifying the path to the saved game.

g = ether_real_estate.load_game(game_path='/home/james/test_game')

Loading the game at path /home/james/test_game

Incrementing the Game

After building and firm bids have been awarded, the remainder of the period runs on autopilot. The following sequence of events occurs:

Firms earn cash flows
Firms pay rent
Players pay taxes (both on income from rent and on property tax for buildings)
Defaulted firms are evicted from buildings
Any unowned buildings are randomly allocated more firms
A new set of subdirectories are created for the next period (e.g. building_bids/1 is added if the game is incrementing to period 1)
All transactions on the blockchain for the ending period are written to the blocks directory for easy inspection

Because the Game was instantiated with the verbose option set to True, the following command will print out lots of details about the incrementation of the game.

g.increment_game()

	 Collected rent:
	 player: p0 eth: 236.44375106499402
	 player: p1 eth: 343.3377445424868
	 player: p2 eth: 426.48224739631064
	 player: p3 eth: 285.5332989721354
	 player: p4 eth: 327.28382811566786
	 End of stage:
	 player: p0 eth: 207.0476726336215
		 B2 ['F36', 'F37', 'F38', 'F39', 'F40', 'F41', 'F42', 'F43', 'F44', 'F45', 'F46', 'F47', 'F48', 'F49', 'F50', 'F51', 'F52', 'F53', 'F283', 'F284'] 20 20
		 B7 ['F126', 'F127', 'F128', 'F129', 'F130', 'F131', 'F132', 'F133', 'F134', 'F135', 'F136', 'F137', 'F138', 'F139', 'F140', 'F141', 'F142', 'F143', 'F287', 'F288'] 20 20
		 B8 ['F144', 'F145', 'F146', 'F147', 'F148', 'F149', 'F150', 'F151', 'F152', 'F153', 'F154', 'F155', 'F156', 'F157', 'F158', 'F159', 'F160', 'F161', 'F286', 'F289'] 20 20
	 player: p1 eth: 324.8122543464084
		 B4 ['F72', 'F73', 'F74', 'F75', 'F76', 'F77', 'F78', 'F79', 'F80', 'F81', 'F82', 'F83', 'F84', 'F85', 'F86', 'F87', 'F88', 'F89', 'F278', 'F281'] 20 20
		 B6 ['F108', 'F109', 'F110', 'F111', 'F112', 'F113', 'F114', 'F115', 'F116', 'F117', 'F118', 'F119', 'F120', 'F121', 'F122', 'F123', 'F124', 'F125', 'F276', 'F280'] 20 20
	 player: p2 eth: 416.9387179845459
		 B5 ['F90', 'F91', 'F92', 'F93', 'F94', 'F95', 'F96', 'F97', 'F98', 'F99', 'F100', 'F101', 'F102', 'F103', 'F104', 'F105', 'F106', 'F107', 'F275', 'F277'] 20 20
	 player: p3 eth: 267.167808776057
		 B3 ['F54', 'F55', 'F56', 'F57', 'F58', 'F59', 'F60', 'F61', 'F62', 'F63', 'F64', 'F65', 'F66', 'F67', 'F68', 'F69', 'F70', 'F71', 'F279', 'F285'] 20 20
		 B9 ['F162', 'F163', 'F164', 'F165', 'F166', 'F167', 'F168', 'F169', 'F170', 'F171', 'F172', 'F173', 'F174', 'F175', 'F176', 'F177', 'F178', 'F179', 'F273', 'F282'] 20 20
	 player: p4 eth: 306.8006908607659
		 B12 ['F216', 'F217', 'F218', 'F219', 'F220', 'F221', 'F222', 'F223', 'F224', 'F225', 'F226', 'F227', 'F228', 'F229', 'F230', 'F231', 'F232', 'F233', 'F271', 'F272'] 20 20
		 B13 ['F234', 'F235', 'F236', 'F237', 'F238', 'F239', 'F240', 'F241', 'F242', 'F243', 'F244', 'F245', 'F246', 'F247', 'F248', 'F249', 'F250', 'F251', 'F270', 'F274'] 20 20
filling building B0
16 20 182
39 <ether_real_estate.firm.Firm object at 0x7fe3287b0160> 2
filling building B1
filling building B10
filling building B11
filling building B14

A snapshot of the blockchain data is shown below.

block_0 = pd.read_csv(g.path + '/blocks/0/blocks.csv')

block_0.head()

	block	from	to	value
0	377	0x0CCcC96D89f2C203939CC0629caEd7C59f2543af	0x0D3A4C9538e94fC132701df365e61466B797f17b	3.493930
1	378	0x0D3A4C9538e94fC132701df365e61466B797f17b	0xe637958EdAB4A6b142a9E7a8F1fC27869A4C893c	6.465874
2	379	0x0D3A4C9538e94fC132701df365e61466B797f17b	0x50D64774DddCDB8c3346b127d211F93F0e2C4CCA	2.366918
3	380	0x0D3A4C9538e94fC132701df365e61466B797f17b	0x2B8a7cE3D79da4a31e175ff360E34B07a3b76b25	23.017025
4	381	0x0D3A4C9538e94fC132701df365e61466B797f17b	0xa553c5C42aCdE9BC5511396FDe73Ce800ff58828	7.903728

The first set of transaction are all from the same account. This is the “banker” account. Firm cash flows arrive from the banker.

block_0.tail()

	block	from	to	value
540	917	0x9394090a2eD31Ec8C88b6926f399f8b719b76E8c	0x0D3A4C9538e94fC132701df365e61466B797f17b	9.543529
541	918	0xC1656f56829cdDC502B53ccDfaa4e29E65040748	0x0D3A4C9538e94fC132701df365e61466B797f17b	10.636863
542	919	0xC1656f56829cdDC502B53ccDfaa4e29E65040748	0x0D3A4C9538e94fC132701df365e61466B797f17b	7.728627
543	920	0x0eBC438EF99b4Ab21D5633037D6B05e44701185a	0x0D3A4C9538e94fC132701df365e61466B797f17b	10.790588
544	921	0x0eBC438EF99b4Ab21D5633037D6B05e44701185a	0x0D3A4C9538e94fC132701df365e61466B797f17b	9.692549

The final set of transactions all send to the banker. That’s because the banker is responsible for collecting taxes from players.

Ending the Game

The game ends whenever the organizer determines enough periods have passed. The winning player is, of course, the one with the most money at the end.

	building	size	firm	industry	firm_size	rent	maturity	cash_holdings
0	B0	20	F0	0	1	4	2	4.0
1	B0	20	F1	1	1	5	2	5.0
2	B0	20	F2	2	1	5	2	5.0
3	B0	20	F3	3	1	4	3	4.0
4	B0	20	F4	4	1	3	3	3.0

	building	size	firm	industry	firm_size	rent	maturity	cash_holdings
0	B0	20	F0	0	1	4	2	4.0
1	B0	20	F1	1	1	5	2	5.0
2	B0	20	F2	2	1	5	2	5.0
3	B0	20	F3	3	1	4	3	4.0
4	B0	20	F4	4	1	3	3	3.0

	building	size	firm	industry	firm_size	rent	maturity	cash_holdings
0	B0	20	F0	0	1	4	2	4.0
1	B0	20	F1	1	1	5	2	5.0
2	B0	20	F2	2	1	5	2	5.0
3	B0	20	F3	3	1	4	3	4.0
4	B0	20	F4	4	1	3	3	3.0