.Real Estate Commission Model.tmp.nlogo

globals
[
  normal
  mean-blue
  mean-yellow
  mean-orange
  mean-year-blue
  mean-year-yellow
  mean-year-orange
]

turtles-own
[
  Firm1                 ;; If true, the person is blue
  Firm2                  ;; If true, the person is yellow or orange
  value               ;; Every agent starts with $0 and gets random amount based on month
  friends             ;; an agent set of all your friends
  groupID             ;; pairs of agents in Firm2
  sales
  income
  income0             ;;last years income
  yearly-income
  points              ;;learning rate

]


to setup
  clear-all

  setup-Firm2
  setup-Firm1
  ask turtles [set shape "person" set sales 0 set income 0]
  move-turtles
  reset-ticks
end

to go
  ask turtles [set income0 income]
  interact
  earn
  get-average
  tick
end

to move-turtles
  ask turtles [
    ifelse color = blue [setxy random 17 (-16 + random 32)][setxy (-1 * random 17) (-16 + random 32)]
  ]
end


to setup-patches
  ask patches [ set pcolor black ]
end

to setup-Firm1
  let num count turtles with [color = yellow or color = orange]
  crt num [set color blue fd random 5 ]
end

to setup-Firm2
  let i 1
  crt 50 [set color yellow ]



  ask turtles with[color = yellow] [
          set i i + 1

          ask patch-here [sprout 1 [    ;;refer 1 person
            set color orange fd random 10
            set groupID i]]

          set friends turtles with [groupID = i]

          fd random 10
              ]




end

to interact

  ask turtles with [color = yellow][
    let interactions random number-of-interactions
    set income income + interactions * 635
    ask friends [set points points + random 9]
  ]

  ask turtles with [color = orange][
    if points >= 8 [set income income + 635 set points points - 9]]
end

to earn
  ask turtles [
   ifelse GoodYear? [set sales random-normal (14177 * num-of-sales-GoodYear) 5000][set sales random-normal (14177 * num-of-sales-BadYear)  5000]]

  ask turtles with [color = blue][
    ifelse sales > 18000
    [set income income + ((sales - 18000) * 0.9 + 18000 * 0.5 )]
  [set income income + 0.5 * sales]]

  ask turtles with [color = yellow or color = orange][set income income + 0.64 * sales]


end



to get-average
  let list-blue []
  let list-yellow []
  let list-orange []

  ask turtles with [color = blue][set list-blue lput income list-blue]
  ask turtles with [color = yellow][set list-yellow lput income list-yellow]
  ask turtles with [color = orange][set list-orange lput income list-orange]

  set mean-blue mean list-blue
  set mean-yellow mean list-yellow
  set mean-orange mean list-orange

  ask turtles [set yearly-income income - income0]

  set list-blue []
  set list-yellow []
  set list-orange []

  ask turtles with [color = blue][set list-blue lput yearly-income list-blue]
  ask turtles with [color = yellow][set list-yellow lput yearly-income list-yellow]
  ask turtles with [color = orange][set list-orange lput yearly-income list-orange]

  set mean-year-blue mean list-blue
  set mean-year-yellow mean list-yellow
  set mean-year-orange mean list-orange




end
@#$#@#$#@
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BUTTON
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Setup
setup
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T
OBSERVER
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BUTTON
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Go
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OBSERVER
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MONITOR
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Firm1
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MONITOR
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150
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KW
count turtles with [color = yellow or color = orange]
17
1
11

PLOT
675
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875
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Average Income
Years
Income
0.0
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0.0
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true
false
"" ""
PENS
"default" 1.0 0 -13345367 true "" "plot mean-blue"
"pen-1" 1.0 0 -1184463 true "" "plot mean-yellow"
"pen-2" 1.0 0 -955883 true "" "plot mean-orange"

MONITOR
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Firm2-OLD-INCOME
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BUTTON
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NIL
go
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T
OBSERVER
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MONITOR
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Firm2-NEW-INCOME
mean-orange
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MONITOR
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L&F
mean-blue
0
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PLOT
677
178
877
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Average Income this Year
Years
Income
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"default" 1.0 0 -14070903 true "" "plot mean-year-blue"
"pen-1" 1.0 0 -1184463 true "" "plot mean-year-yellow"
"pen-2" 1.0 0 -955883 true "" "plot mean-year-orange"

SLIDER
6
215
183
248
number-of-interactions
number-of-interactions
0
50
25.0
1
1
NIL
HORIZONTAL

SWITCH
15
383
131
416
GoodYear?
GoodYear?
0
1
-1000

SLIDER
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269
186
302
num-of-sales-GoodYear
num-of-sales-GoodYear
0
10
2.0
1
1
NIL
HORIZONTAL

SLIDER
9
323
181
356
num-of-sales-BadYear
num-of-sales-BadYear
0
10
1.0
1
1
NIL
HORIZONTAL

@#$#@#$#@
## WHAT IS IT?

This agent-based model intends to compare two real estate commission policies by examining the motivation of agents in two companies. The motivation of agents is set based on their income. Both firms follow the 3% break down of the settlement price of property. The difference is observed in the intercompany commission split. Firm 1 rewards its realtors by changing the 50%-50% split into 90%-10% when the agents reach $18,000 in commission. Firm 2 rewards its realtors by setting a higher share of 64%-46% for them and applying a ‘profit sharing’ method. The significance of this model is that it compares two strategies in a competitive field, allowing for a clearer and fact based decision making for realtors and an understanding of realtors’ financial incentive for clients.

## HOW IT WORKS

This model, includes two types of agents: firm 1 (blue) and firm 2 (yellow and orange). Firm 1 has 100 agents and firm 2 has two sets of 50 agents that are paired and interact with one another. As mentioned in the assumptions, firm 2 has endorsers and endorsees whose interactions effect their income. All agents are setup to begin with $0 income and no sales. Then an amount of commission earning is randomly distributed among them based on the average commission data. This distribution range is different in good and bad years. All agents have sales, income, earn and yearly income as their variables. In addition to these, firm 2 also has friends, group ID and points as its defining variables. The friend variable determines the random pairing of firm 2 agents to interact in groups. Every group has an ID and consists of one endorser (yellow) and one endorsee (orange). The model allocates a random number of interactions between 0 and 9 and counts every time the agents with the same group ID interact. Every interaction adds two points to the endorsee and every 8 interaction adds $635 to the endorsee’s income. Every time the endorsee makes a transaction, the endorser is compensated $200 by the company.
The implementation of our assumptions, variables, rules and interactions can be observed in the model through the behavior of agents. All the commands coded in the model are ultimately calculated by the ‘get-average’ function. This function gets the average cumulative income and the average income this year of all agents in both firms. The get-average functions through the global variables calculating the mean and mean per year of all agents in each firm.


## HOW TO USE IT

the setup button does the following commands in order: clear-all, setup-KW, setup-L&F, ask turtles [set shape "person" set sales 0 set income 0], move-turtles, reset-ticks.
The Go button does the following commands in order: ask turtles [set income0 income], interact, earn, get-average, tick.
The number-of-interaction slider sets the number of interactions between yellow and orange agents.
The sliders num-of-sales-GoodYear and num-of-sales-BadYear set the number of sales in good and bad years for all the 200 agents.
The GoodYear? switch determines the range of commissions to be distributed based on a good or bad year.
The plots show the average cumulative income and average income for this year for all the agents of every firm.
The monitors KW-OLD-INCOME, KW-NEW-INCOME and L&F demonstrate the exact amount earned by every agent set.

## THINGS TO NOTICE

The model is partially build based on real world data and partially (profit sharing details) created.

## THINGS TO TRY

Play with the parameters to see the different outcomes. Change the commission splits to see the effect of the change.

## EXTENDING THE MODEL

Although this model demonstrates a targeted comparison between two real estate commission policies, it can be extended to resonate the reality much more. By adding new interactions, parameters and defining the environment to the code, the model can expand to produce even more realistic data. If we introduce ability, effort, work hours and personality types to the model as new parameters, we can systematically measure motivation, productivity and satisfaction. Performance can be defined as a product of agent ability, effort and work hours. By gathering data on the definition of average performance in firms, we can calculate the overall productivity of agents. The agent productivity can act as the indicator of motivation and satisfaction. Personality types of agents can be introduced in groups of hard working and lazy and randomly distributed. Personality types will affect the performance of agents and therefore change the income results. Creating a social network model will help us understand the agent relationships, interactions and influences more specifically.

## NETLOGO FEATURES
The pairing of yellow and orange agents after breeding the orange agents is an interesting feature.

## RELATED MODELS

Not determined!

## CREDITS AND REFERENCES

Housing Statistics. (n.d.). Retrieved 2015, from http://www.mdrealtor.org/ResearchStats/HousingStatistics.aspx

Keller Williams Realty: The Commission Structure - Real Estate Careers at Keller Williams Realty. (n.d.). Retrieved 2015, from http://moving-careers.com/keller-williams-realty-the-commission-structure/

Montgomery County Home Sales Statistics. (n.d.). Retrieved December 18, 2015, from https://gcaar.com/toolkit_ektid2030.aspx

Real Estate Sales Agents. (n.d.). Retrieved December 18, 2015, from http://www.rileyguide.com/careers/real-estate-agents.shtml

Wise, C. (1997, March 1). Unbundling Services for Salespeople. Retrieved December 18, 2015, from http://realtormag.realtor.org/for-brokers/feature/article/1997/03/unbundling-services-for-salespeople
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@#$#@#$#@
NetLogo 6.1.0
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="experiment" repetitions="20" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="1"/>
    <metric>mean-year-blue</metric>
    <metric>mean-year-yellow</metric>
    <metric>mean-year-orange</metric>
    <enumeratedValueSet variable="number-of-interactions">
      <value value="25"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="GoodYear?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="num-of-sales-BadYear">
      <value value="1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="num-of-sales-GoodYear">
      <value value="2"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
@#$#@#$#@
@#$#@#$#@
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