Rolling equity and cash flow all real estate investments

rolling_equity_cash_flow_all_investments.jl

using Dates
using DataFrames
using Gadfly
using CSV
using Statistics

### methodology
# 1. amoritzation schedule per property
# 2. initial down payment + principal + any estimated equity due to renovations  (conservative estimates)
# 3. account for any refinances - amooritization schedule for each and join
# 4. extend data set out to the mortage with the farthest out expiration date
# 5. discoutning any rent raise over time & property appreciation (generally with inflation)(in turn would increase cash flow and property valuations)
###

# todays data
t_date = round(today(), Dates.Month)

# Properties
high_meadow = "high_meadow"
ave_m = "ave_m"
ave_m_refi = "yes" # "yes", "no"
ave_o = "ave_o"
ave_m_half = "ave_m_half"

# square footage
high_mead_sq_ft = 1631.00
ave_m_sq_ft = 2857.58
ave_o_sq_ft = 2168.1
ave_m_half_sq_ft = 2556

high_mead_newVal = 135 * high_mead_sq_ft
ave_m_newVal = 116 * ave_m_sq_ft
ave_o_newVal = 110 * ave_o_sq_ft
ave_m_half_newVal = 110 * ave_m_half_sq_ft

high_mead_equity_raise = high_mead_newVal - 142000
ave_m_equity_raise = ave_m_newVal - 225000
ave_o_equity_raise = ave_o_newVal - 170000
ave_m_half_equity_raise = ave_m_half_newVal - 215000

high_mead_equity_raise + ave_m_equity_raise + ave_o_equity_raise + ave_m_half_equity_raise

# renovation dates
high_mead_reno_date = Date("2019-01-01", dateformat"y-m-d")
ave_m_reno_date = Date("2019-08-01", dateformat"y-m-d")
ave_o_reno_date = Date("2020-05-01", dateformat"y-m-d")
ave_m_half_reno_date = Date("2020-09-01", dateformat"y-m-d")

# purchase dates
high_mead_pur = Date("2016-11-04", dateformat"y-m-d")
high_mead_round_date = round(high_mead_pur, Dates.Month)
ave_m_pur = Date("2019-03-15", dateformat"y-m-d")
ave_m_round_date = Date("2019-04-01", dateformat"y-m-d") # pre paid the interest to the nearest date at closing
ave_m_refinance_date = Date("2020-05-27", dateformat"y-m-d")
ave_m_refinance_round_date = Date("2020-06-01", dateformat"y-m-d")
ave_o_pur = Date("2019-12-29", dateformat"y-m-d")
ave_o_round_date = Date("2020-01-01", dateformat"y-m-d")
ave_m_half_pur = Date("2020-08-25", dateformat"y-m-d")
ave_m_half_round_date = Date("2020-09-01", dateformat"y-m-d")

# date range
# ave_m_dr = collect(ave_m_pur:Month(1):Date(2015,2,3))

# Create amoritzation schedule for each property

##################################
###  5015 Avenue M
##################################
## prior to refinance

# Step 1 - Gather the information you need to calculate the loan's amortization
property_value = 225000
loan_amount = .75
down_payment = property_value-(loan_amount * property_value)
debt_borrowed = property_value - down_payment
interest_rate = 0.05625 # annual interest rate
loan_term = 360 # months
ave_m_dr = collect(ave_m_round_date:Month(1):(Date(ave_m_round_date)+Dates.Month(358)))

# Calculate the principal / interest over the loan term
# Initialize outputs
remaining_loan_amount = collect(1.0:1.0:loan_term-1)
interest = zeros(size(remaining_loan_amount,1))
principal = zeros(size(remaining_loan_amount,1))
interest_principal = zeros(size(remaining_loan_amount,1))
total_equity = zeros(size(remaining_loan_amount,1))
# for loop to create amortization schedule
i=1
for i = 1:size(remaining_loan_amount,1)
  # set the first row at 1 = 1 - this is the first principal and interest payment
  if i == 1
    I = debt_borrowed *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term)))) - I # Calculate the interest + principal then subtract interest
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = debt_borrowed - P
    total_equity[i] = down_payment
   else
    # Using the data from the first row - continue to pay down princial and debt
    I = remaining_loan_amount[i-1] *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term-i)))) - I # Calculate the interest + principal then subtract interest also note loan_term-i = reducing the loan term through the course of the loan
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = remaining_loan_amount[i-1] - P
    total_equity[i] = total_equity[i-1] + P
    print("This is Month",i,"\nAmoritization Schedule\n")
end
end

# rolling sums
cumsum_interest = cumsum(interest)
cumsum_principal = cumsum(principal)
cumsum_interest_principal = cumsum(interest_principal)
cumsum_remaining_loan_amount = cumsum(remaining_loan_amount)

# build data frames
labels = fill(ave_m,size(total_equity,1))
df = hcat(ave_m_dr,interest,principal,interest_principal,remaining_loan_amount,cumsum_interest,cumsum_principal,cumsum_interest_principal,total_equity,labels)
names = ["date","interest","principal","interest_principal","remaining_loan_amount","cumulative_interest","cumulative_principal","cumulative_interest_principal","total_equity","labels"]
ave_m_df = DataFrame(df)
rename!(ave_m_df, names)

### refinance

# Step 1 - Gather the information you need to calculate the loan's amortization
property_value = 225000
loan_amount = .75
down_payment = property_value-(loan_amount * property_value)
debt_borrowed = 177616 # per closing dicosure
interest_rate = 0.03625 # annual interest rate
loan_term = 360 # months
ave_m_dr = collect(ave_m_refinance_round_date:Month(1):(Date(ave_m_refinance_round_date)+Dates.Month(358)))

# Calculate the principal / interest over the loan term
# Initialize outputs
remaining_loan_amount = collect(1.0:1.0:loan_term-1)
interest = zeros(size(remaining_loan_amount,1))
principal = zeros(size(remaining_loan_amount,1))
interest_principal = zeros(size(remaining_loan_amount,1))
total_equity = zeros(size(remaining_loan_amount,1))
# for loop to create amortization schedule
i=1
for i = 1:size(remaining_loan_amount,1)
  # set the first row at 1 = 1 - this is the first principal and interest payment
  if i == 1
    I = debt_borrowed *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term)))) - I # Calculate the interest + principal then subtract interest
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = debt_borrowed - P
    total_equity[i] = down_payment
   else
    # Using the data from the first row - continue to pay down princial and debt
    I = remaining_loan_amount[i-1] *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term-i)))) - I # Calculate the interest + principal then subtract interest also note loan_term-i = reducing the loan term through the course of the loan
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = remaining_loan_amount[i-1] - P
    total_equity[i] = total_equity[i-1] + P
    print("This is Month",i,"\nAmoritization Schedule\n")
end
end

# rolling sums
cumsum_interest = cumsum(interest)
cumsum_principal = cumsum(principal)
cumsum_interest_principal = cumsum(interest_principal)
cumsum_remaining_loan_amount = cumsum(remaining_loan_amount)

# build data frames
labels = fill(ave_m,size(total_equity,1))
df_refi = hcat(ave_m_dr,interest,principal,interest_principal,remaining_loan_amount,cumsum_interest,cumsum_principal,cumsum_interest_principal,total_equity,labels)
names = ["date","interest","principal","interest_principal","remaining_loan_amount","cumulative_interest","cumulative_principal","cumulative_interest_principal","total_equity","labels"]
ave_m_df_refi = DataFrame(df_refi)
rename!(ave_m_df_refi, names)

# Split data frames to make one to account for the refinance
df_1 = ave_m_df[(ave_m_df[:date].<ave_m_refinance_round_date),:]
df_final = append!(df_1,ave_m_df_refi)
df_final.cumulative_interest = cumsum(df_final.interest)
df_final.cumulative_principal = cumsum(df_final.principal)
df_final.cumulative_interest_principal = cumsum(df_final.interest_principal)
df_final.total_equity[1] = down_payment
for i = 2:size(df_final.total_equity,1)
    df_final.total_equity[i] = df_final.total_equity[i-1] + df_final.principal[i]
end

df_final.total_equity_newVal = zeros(size(df_final.total_equity,1))
index_pos = findfirst(isequal(ave_m_reno_date), df_final.date)
i=6
for i = 1:size(df_final.total_equity_newVal,1)
    if i < index_pos
    df_final.total_equity_newVal[i] = df_final.total_equity[i]
else
    df_final.total_equity_newVal[i] = (ave_m_newVal - property_value) + df_final.total_equity[i]
end
end

# plot data using gadfly
plot(x=df_final.date,y=df_final.total_equity,Scale.y_continuous(format=:plain),Geom.line)

# obtain ytd total equity
index_pos = findfirst(isequal(t_date), df_final.date)
ave_m_total_equity = df_final.total_equity[index_pos]

##################################
###  3719 avenue o
##################################

# Step 1 - Gather the information you need to calculate the loan's amortization
property_value = 170000
loan_amount = .75
down_payment = property_value-(loan_amount * property_value)
debt_borrowed = property_value - down_payment
interest_rate = 0.05125 # annual interest rate
loan_term = 360 # months
ave_o_dr = collect(ave_o_round_date:Month(1):(Date(ave_o_round_date)+Dates.Month(358)))

# Calculate the principal / interest over the loan term
# Initialize outputs
remaining_loan_amount = collect(1.0:1.0:loan_term-1)
interest = zeros(size(remaining_loan_amount,1))
principal = zeros(size(remaining_loan_amount,1))
interest_principal = zeros(size(remaining_loan_amount,1))
total_equity = zeros(size(remaining_loan_amount,1))
# for loop to create amortization schedule
i=1
for i = 1:size(remaining_loan_amount,1)
  # set the first row at 1 = 1 - this is the first principal and interest payment
  if i == 1
    I = debt_borrowed *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term)))) - I # Calculate the interest + principal then subtract interest
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = debt_borrowed - P
    total_equity[i] = down_payment
   else
    # Using the data from the first row - continue to pay down princial and debt
    I = remaining_loan_amount[i-1] *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term-i)))) - I # Calculate the interest + principal then subtract interest also note loan_term-i = reducing the loan term through the course of the loan
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = remaining_loan_amount[i-1] - P
    total_equity[i] = total_equity[i-1] + P
    print("This is Month",i,"\nAmoritization Schedule\n")
end
end

# rolling sums
cumsum_interest = cumsum(interest)
cumsum_principal = cumsum(principal)
cumsum_interest_principal = cumsum(interest_principal)
cumsum_remaining_loan_amount = cumsum(remaining_loan_amount)

# build data frames
labels = fill(ave_o,size(total_equity,1))
df = hcat(ave_o_dr,interest,principal,interest_principal,remaining_loan_amount,cumsum_interest,cumsum_principal,cumsum_interest_principal,total_equity,labels)
names = ["date","interest","principal","interest_principal","remaining_loan_amount","cumulative_interest","cumulative_principal","cumulative_interest_principal","total_equity","labels"]
ave_o_df = DataFrame(df)
rename!(ave_o_df, names)

ave_o_df.total_equity[1] = down_payment
for i = 2:size(ave_o_df.total_equity,1)
    ave_o_df.total_equity[i-1] = ave_o_df.total_equity[i-1] + ave_o_df.principal[i]
end

ave_o_df.total_equity_newVal = zeros(size(ave_o_df.total_equity,1))
index_pos = findfirst(isequal(ave_o_reno_date), ave_o_df.date)
for i = 1:size(ave_o_df.total_equity_newVal,1)
    if i < index_pos
        ave_o_df.total_equity_newVal[i] = ave_o_df.total_equity[i]
    else
    ave_o_df.total_equity_newVal[i] = (ave_o_newVal - property_value) + ave_o_df.total_equity[i]
end
end


# obtain ytd total equity
index_pos = findfirst(isequal(t_date), ave_o_df.date)
ave_m_total_equity = ave_o_df.total_equity[index_pos]

##################################
###  high meadow
##################################

# Step 1 - Gather the information you need to calculate the loan's amortization
property_value = 142000
loan_amount = .8
down_payment = property_value-(loan_amount * property_value)
debt_borrowed = property_value - down_payment
interest_rate = 0.0399 # annual interest rate
loan_term = 360 # months
high_mead_dr = collect(high_mead_round_date:Month(1):(Date(high_mead_round_date)+Dates.Month(358)))

# Calculate the principal / interest over the loan term
# Initialize outputs
remaining_loan_amount = collect(1.0:1.0:loan_term-1)
interest = zeros(size(remaining_loan_amount,1))
principal = zeros(size(remaining_loan_amount,1))
interest_principal = zeros(size(remaining_loan_amount,1))
total_equity = zeros(size(remaining_loan_amount,1))
# for loop to create amortization schedule
i=1
for i = 1:size(remaining_loan_amount,1)
  # set the first row at 1 = 1 - this is the first principal and interest payment
  if i == 1
    I = debt_borrowed *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term)))) - I # Calculate the interest + principal then subtract interest
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = debt_borrowed - P
    total_equity[i] = down_payment
   else
    # Using the data from the first row - continue to pay down princial and debt
    I = remaining_loan_amount[i-1] *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term-i)))) - I # Calculate the interest + principal then subtract interest also note loan_term-i = reducing the loan term through the course of the loan
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = remaining_loan_amount[i-1] - P
    total_equity[i] = total_equity[i-1] + P
    print("This is Month",i,"\nAmoritization Schedule\n")
end
end

# rolling sums
cumsum_interest = cumsum(interest)
cumsum_principal = cumsum(principal)
cumsum_interest_principal = cumsum(interest_principal)
cumsum_remaining_loan_amount = cumsum(remaining_loan_amount)

# build data frames
labels = fill(high_meadow,size(total_equity,1))
df = hcat(high_mead_dr,interest,principal,interest_principal,remaining_loan_amount,cumsum_interest,cumsum_principal,cumsum_interest_principal,total_equity,labels)
names = ["date","interest","principal","interest_principal","remaining_loan_amount","cumulative_interest","cumulative_principal","cumulative_interest_principal","total_equity","labels"]
high_mead_df = DataFrame(df)
rename!(high_mead_df, names)

# calculate final equity inclsuive of princiapl and downpayment
high_mead_df.total_equity[1] = down_payment
for i = 2:size(high_mead_df.total_equity,1)
    high_mead_df.total_equity[i-1] = high_mead_df.total_equity[i-1] + high_mead_df.principal[i]
end

high_mead_df.total_equity_newVal = zeros(size(high_mead_df.total_equity,1))
index_pos = findfirst(isequal(high_mead_reno_date), high_mead_df.date)
for i = 1:size(high_mead_df.total_equity_newVal,1)
    if i < index_pos
        high_mead_df.total_equity_newVal[i] = high_mead_df.total_equity[i]
    else
    high_mead_df.total_equity_newVal[i] = (high_mead_newVal - property_value) + high_mead_df.total_equity[i]
end
end

# obtain ytd total equity
index_pos = findfirst(isequal(t_date), high_mead_df.date)
high_mead_df_total_equity = high_mead_df.total_equity[index_pos]

##################################
###  5328 ave m half
##################################

# Step 1 - Gather the information you need to calculate the loan's amortization
property_value = 215000
loan_amount = .75
down_payment = property_value-(loan_amount * property_value)
debt_borrowed = property_value - down_payment
interest_rate = 0.035 # annual interest rate
loan_term = 360 # months
ave_m_half_dr = collect(ave_m_half_round_date:Month(1):(Date(ave_m_half_round_date)+Dates.Month(358)))

# Calculate the principal / interest over the loan term
# Initialize outputs
remaining_loan_amount = collect(1.0:1.0:loan_term-1)
interest = zeros(size(remaining_loan_amount,1))
principal = zeros(size(remaining_loan_amount,1))
interest_principal = zeros(size(remaining_loan_amount,1))
total_equity = zeros(size(remaining_loan_amount,1))
# for loop to create amortization schedule
i=1
for i = 1:size(remaining_loan_amount,1)
  # set the first row at 1 = 1 - this is the first principal and interest payment
  if i == 1
    I = debt_borrowed *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term)))) - I # Calculate the interest + principal then subtract interest
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = debt_borrowed - P
    total_equity[i] = down_payment
   else
    # Using the data from the first row - continue to pay down princial and debt
    I = remaining_loan_amount[i-1] *(interest_rate/12) # note converting annual interest to monthly (interest_rate/12)
    P = (I / (1-(1/(1+interest_rate/12)^(loan_term-i)))) - I # Calculate the interest + principal then subtract interest also note loan_term-i = reducing the loan term through the course of the loan
    IP = I + P
    interest[i] = I
    principal[i] = P
    interest_principal[i] = IP
    remaining_loan_amount[i] = remaining_loan_amount[i-1] - P
    total_equity[i] = total_equity[i-1] + P
    print("This is Month",i,"\nAmoritization Schedule\n")
end
end

# rolling sums
cumsum_interest = cumsum(interest)
cumsum_principal = cumsum(principal)
cumsum_interest_principal = cumsum(interest_principal)
cumsum_remaining_loan_amount = cumsum(remaining_loan_amount)

# build data frames
labels = fill(ave_m_half,size(total_equity,1))
df = hcat(ave_m_half_dr,interest,principal,interest_principal,remaining_loan_amount,cumsum_interest,cumsum_principal,cumsum_interest_principal,total_equity,labels)
names = ["date","interest","principal","interest_principal","remaining_loan_amount","cumulative_interest","cumulative_principal","cumulative_interest_principal","total_equity","labels"]
ave_m_half_df = DataFrame(df)
rename!(ave_m_half_df, names)

# calculate final equity inclsuive of princiapl and downpayment
ave_m_half_df.total_equity[1] = down_payment
for i = 2:size(ave_m_half_df.total_equity,1)
    ave_m_half_df.total_equity[i-1] = ave_m_half_df.total_equity[i-1] + ave_m_half_df.principal[i]
end

ave_m_half_df.total_equity_newVal = zeros(size(ave_m_half_df.total_equity,1))
index_pos = findfirst(isequal(ave_m_half_reno_date), ave_m_half_df.date)
for i = 1:size(ave_m_half_df.total_equity_newVal,1)
    if i < index_pos
    ave_m_half_df.total_equity_newVal[i] = ave_m_half_df.total_equity[i]
else
    ave_m_half_df.total_equity_newVal[i] = (ave_m_half_newVal - property_value) + ave_m_half_df.total_equity[i]

end
end

# obtain ytd total equity
#index_pos = findfirst(isequal(t_date), ave_m_half_df.date)
#ave_m_half_df_total_equity = ave_m_half_df.total_equity[index_pos]

#################
# join data frames for total rolling equity
#################

all_df = join(high_mead_df, df_final, on = :date, kind = :outer,makeunique = true)
all_df.all_equity = all_df.total_equity .+ all_df.total_equity_1
all_df = join(all_df, ave_o_df, on = :date, kind = :outer,makeunique = true)
all_df.all_equity = all_df.all_equity .+ all_df.total_equity_2
all_df = join(all_df, ave_m_half_df, on = :date, kind = :outer,makeunique = true)
all_df = coalesce.(all_df, 0.0)

# carry forward final equities post mortgage pay off
for i = 2:nrow(all_df)
    if all_df.total_equity[i-1] != 0.0 && all_df.total_equity[i] == 0.0
        all_df.total_equity[i] = all_df.total_equity[i-1]
    end
    if all_df.total_equity_1[i-1] != 0.0 && all_df.total_equity_1[i] == 0.0
        all_df.total_equity_1[i] = all_df.total_equity_1[i-1]
    end
    if all_df.total_equity_2[i-1] != 0.0 && all_df.total_equity_2[i] == 0.0
        all_df.total_equity_2[i] = all_df.total_equity_2[i-1]
    end
    if all_df.total_equity_3[i-1] != 0.0 && all_df.total_equity_3[i] == 0.0
        all_df.total_equity_3[i] = all_df.total_equity_3[i-1]
    end
    # new vals
    if all_df.total_equity_newVal[i-1] != 0.0 && all_df.total_equity_newVal[i] == 0.0
        all_df.total_equity_newVal[i] = all_df.total_equity_newVal[i-1]
    end
    if all_df.total_equity_newVal_1[i-1] != 0.0 && all_df.total_equity_newVal_1[i] == 0.0
        all_df.total_equity_newVal_1[i] = all_df.total_equity_newVal_1[i-1]
    end
    if all_df.total_equity_newVal_2[i-1] != 0.0 && all_df.total_equity_newVal_2[i] == 0.0
        all_df.total_equity_newVal_2[i] = all_df.total_equity_newVal_2[i-1]
    end
    if all_df.total_equity_newVal_3[i-1] != 0.0 && all_df.total_equity_newVal_3[i] == 0.0
        all_df.total_equity_newVal_3[i] = all_df.total_equity_newVal_3[i-1]
    end
end

all_df.all_equity = all_df.total_equity .+ all_df.total_equity_1 .+ all_df.total_equity_2 .+ all_df.total_equity_3 # sum all ending equity
all_df.all_equity_newVal = all_df.total_equity_newVal .+ all_df.total_equity_newVal_1 .+ all_df.total_equity_newVal_2 .+ all_df.total_equity_newVal_3 # sum all ending equity


# find total equity across all properties YTD
index_pos = findfirst(isequal(t_date+Dates.Month(1)), all_df.date)
all_df_total_equity = all_df.all_equity[index_pos]

line_1 = all_df.all_equity
label_1 = fill("all_equity",size(line_1,1))
line_2 = all_df.all_equity_newVal
label_2 = fill("all_equity_post_renovation",size(line_2,1))
label_plot = vcat(label_1,label_2)
all_plot = vcat(line_1,line_2)
plot_df = DataFrame(hcat(label_plot,all_plot))
names = ["data","label"]
rename!(plot_df,names)

# plot data using gadfly
theticks = collect(0:100000:Int64.(round(maximum(all_df.all_equity_newVal),digits=0)))
plot(layer(x=all_df.date,y=all_df.all_equity,Geom.line, Theme(default_color=colorant"blue"),
xintercept=[high_mead_round_date,ave_m_round_date,ave_o_round_date,ave_m_half_round_date], Geom.vline(color=["red","red","red","red"])),
layer(x=all_df.date,y=all_df.all_equity_newVal,Geom.line,Theme(default_color=colorant"green")),
Scale.y_continuous(format=:plain),
Theme(background_color="white"),
Guide.title("Real estate total equity as a function of time"),
Guide.xlabel("Time"),
Guide.ylabel("Equity"),
Guide.yticks(ticks = theticks))


# subset data frame
subset_df = all_df[(all_df[:date].< (t_date+Dates.Month(2))),:]
theticks = collect(0:100000:Int64.(round(maximum(subset_df.all_equity_newVal)+100000,digits=0)))
plot(layer(x=subset_df.date,y=subset_df.all_equity,Geom.line, Theme(default_color=colorant"blue"),
xintercept=[high_mead_round_date,ave_m_round_date,ave_o_round_date,ave_m_half_round_date], Geom.vline(color=["red","red","red","red"])),
layer(x=subset_df.date,y=subset_df.all_equity_newVal,Geom.line,Theme(default_color=colorant"green")),
Scale.y_continuous(format=:plain),
Theme(background_color="white"),
Guide.title("Real estate total equity as a function of time"),
Guide.xlabel("Time"),
Guide.ylabel("Equity"),
Guide.yticks(ticks = theticks),
Guide.manual_color_key("Legend", ["Initial Equity", "Equity Post Renovation"], ["blue", "green"]))

# rate of change
length_of_time = (subset_df.date[size(subset_df.date,1)] - subset_df.date[1])
monthly_ROC = (subset_df.all_equity_newVal[size(subset_df.all_equity_newVal,1)] - subset_df.all_equity_newVal[1]) /  56.76 # change in $ / time

length_of_time = (subset_df.date[size(subset_df.date,1)] - subset_df.date[1])
monthly_ROC = (subset_df.all_equity[size(subset_df.all_equity,1)] - subset_df.all_equity[1]) /  56.76 # change in $ / time

####################################
# Cash flow
###################################



# export to .csv
 # CSV.write("C:/Users/Andrew.Bannerman/Desktop/Real Estate/All_Properties/all_df.csv", all_df;delim=',')