knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(rio)
library(rmiscutils)
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## Attaching package: 'rmiscutils'
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## add_row
library(lubridate)
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## Attaching package: 'lubridate'
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## date
library(scales)
library(DT)
This is downloaded from the County Auditor and Recorder’s Data. This can be any spreadsheet of county data as long as it has the parcel number, mortgage amount, tax district, effective rate, owner name, sale date, and (Auditor’s) market value.
Note: the Warren County Auditor’s website lists 4,606 properties with a land use code > 199 and < 500 whereas our data pulled up 4860.
This is just for commerical properties with land use codes >199 and <500.
#importing data
Commercial_Properties <- rio::import("Warren County Properties Last Verified 7-21-19.csv") #OR each County
#R doesn't like commas in the data, and won't read that as a number so this changes it.
Commercial_Properties$Amount_Owed_to_County <- rmiscutils::number_with_commas(Commercial_Properties$Amount_Owed_to_County)
Commercial_Properties$mortgage_amount <- rmiscutils::number_with_commas(Commercial_Properties$mortgage_amount)
Commercial_Properties$True_Value <- rmiscutils::number_with_commas(Commercial_Properties$True_Value)
Commercial_Properties$Mortgage_Share <- rmiscutils::number_with_commas(Commercial_Properties$Mortgage_Share)
Commercial_Properties$New_Taxable_Value <- rmiscutils::number_with_commas(Commercial_Properties$New_Taxable_Value)
Commercial_Properties$Auditors_Total_Value_of_Parcels <- rmiscutils::number_with_commas(Commercial_Properties$Auditors_Total_Value_of_Parcels)
#As per the auditor checkint our math on this, we have to take out the exempt properties
Commercial_Properties <- filter(Commercial_Properties, taxable_value>0)
What is the amount owed to each county?
note: in theory we would just add all the taxes_paid and there’s always going to be a certain percentage of people behind on their taxes, and we’re not interested in that. So here we are calculating the total amount to taxes collected in theory
Commercial_Properties <- mutate(Commercial_Properties, Amount_Owed_to_County=market_value*`Effective Rate`*.35*.001)
How much does each district raise?
tax_district | Total_Per_District |
---|---|
01 - CLEARCREEK TWP-SPRINGBORO CSD | 276407.0498 |
02 - CLEARCREEK TWP-LEBANON CSD | 44785.4039 |
03 - CLEARCREEK TWP-WAYNE LSD | 18493.4568 |
04 - CLEARCREEK TWP LCSD - FLD | 33938.7748 |
05 - CLEARCREEK TWP-SPRINGBORO CORP | 3959707.3200 |
06 - CLEARCREEK TWP-SPRBORO CSD FLD | 159238.0339 |
07 - CLEARCREEK TWP-SPRINGBORO CORP | 2139102.9752 |
08 - DEERFIELD TWP-MASON CSD | 9045914.4227 |
09 - DEERFIELD TWP-KINGS LSD | 7143797.0826 |
11 - MASON CORP - MASON CSD | 9866944.1058 |
12 - MASON CORP - KINGS LSD | 3959231.7295 |
14 - FRANKLIN TWP-FRANKLIN CSD | 139402.9400 |
15 - FRANKLIN TWP-MIDDLETOWN CSD | 14397.5473 |
16 - FRANKLIN TWP-CARLISLE LSD | 89167.2141 |
18 - FRANKLIN TWP-CARLISLE CORP | 650142.8949 |
19 - FRANKLIN TWP-CARLISLE CORP-FSD | 51110.6924 |
21 - FRANKLIN CITY FCSD | 4736595.7417 |
23 - MIDDLETOWN CORP-MIDDLETOWN CSD | 2901139.5129 |
24 - FRANKLIN TWP-MIDDLETOWN CO-FSD | 667895.7600 |
26 - HAMILTON TWP-LITTLE MIAMI LSD | 1898174.8362 |
27 - HAMILTON TWP-LOVELAND CSD | 2265.1534 |
28 - HAMILTON TWP-KINGS LSD | 53151.8932 |
30 - LOVELAND CORP-LOVELAND CSD | 34574.4000 |
31 - HAMILTON TWP-SO. LEBANON CORP | 173150.7301 |
32 - HAMILTON TWP-MAINEVILLE CORP | 167734.4306 |
33 - LOVELAND CORP-LITTLE MIAMI LSD | 33791.1165 |
35 - HARLAN TWP-LITTLE MIAMI LSD | 19712.9285 |
36 - HARLAN TWP-BLANCHESTER LSD | 5014.6633 |
37 - HARLAN TWP-GOSHEN LSD | 107115.2722 |
39 - HARLAN TWP-BUTLERVILLE CORP | 2319.9960 |
40 - HARLAN TWP-PLEASANT PLAIN CORP | 9614.4231 |
41 - HARLAN TWP-BLANCHESTER CORP. | 3373.3819 |
43 - MASSIE TWP-CLINTON MASSIE LSD | 1472.3685 |
45 - MASSIE TWP-HARVEYSBURG CORP | 59377.6666 |
48 - SALEM TWP-LITTLE MIAMI LSD | 110467.8748 |
50 - SALEM TWP-MORROW CORP | 337565.7531 |
51 - TURTLECRK-MIDDLETOWN CORP-MCSD | 173880.3189 |
52 - TURTLECRK-MIDDLETOWN CORP-LCSD | 80608.0845 |
53 - TURTLECREEK TWP-LEBANON CSD | 1029819.3867 |
55 - TURTLECREEK TWP-MIDDLETOWN CSD | 85484.4984 |
56 - TURTLECREEK TWP-LITTLE MIAMI | 657.2912 |
58 - TRTLCRK TP-MNROE CP-MONROE LSD | 1283883.5724 |
59 - TRTLCRK TP-MNROE CP-MONROE LSD | 253106.4641 |
61 - LEBANON CORP-LEBANON C.S.D. | 5454171.4840 |
62 - LEBANON CORP.-KINGS LSD | 626218.0218 |
63 - UNION TWP-LEBANON CSD | 6016.0728 |
64 - UNION TWP-LITTLE MIAMI LSD | 34580.5948 |
65 - UNION TWP-MASON CSD | 297.3953 |
66 - UNION TWP-KINGS LSD | 130340.3836 |
67 - UNION TWP-LEB CORP-KINGS LSD | 1899.0048 |
68 - UNION TWP-SOUTH LEBANON CORP | 313147.2500 |
70 - WASHINGTON TWP-LMLSD- CWJ FIRE | 10677.9250 |
71 - WASHINGTON TWP-LITTLE MIAMI LS | 7545.5789 |
72 - WASHINGTON TWP-CLINTON MASSIE | 8490.9766 |
73 - WASHINGTON TWP-LEBANON CSD | 11952.9158 |
74 - WASHINGTON TWP-CMLSD- CWJ FIRE | 830.8550 |
76 - WAYNE TWP-WAYNE LSD | 198740.2914 |
80 - WAYNE TWP-CORWIN CORP-WAYNE LS | 77237.1464 |
81 - WAYNE TWP-WAYNESVILLE CORP | 551075.1352 |
85 - WASH TWP CMLSD CWJFD MLCLD | 18240.8543 |
92 - WASH TWP LMLSD MLCLD | 35854.4113 |
94 - DEERFIELD TWP- MASON CORP-KLSD | 1494596.3995 |
95 - TURTLCRK TWP LEB CORP LEB CSD | 23477.0782 |
How much is that altogether?
sum(na.omit(Amount_Due_Per_Tax_District$Total_Per_District))
## [1] 60829119
That same number, but written as currency with commas so you can read:
scales::dollar(.Last.value)
## character(0)
First, our data is in the wrong format. When the date, owner, and amount are the same, the sale amount is actually for ALL the properties together. But there are different parcel numbers because large properties are usually composed of several parcels next to each other.
So for example, parcels 123, 456, and 789 are are listed as sold to Company LLC on June 22, 2019 for 6 million dollars. But each is not worth 6 million. They are worth 1, million, 2 million, and 3 million dollars respectively. All the properties together are worth valued at 6 million dollars, BUT the mortgage amount field on each reads 6 million dollars. And we don’t want to accidentally log it as 18 million dollars.
So here we will group by owner, date of mortgage, and the mortgage amount. But then we run into another problem: Some of these have multiple tax districts:
Looking_For_Sort_of_dupulicates <- Commercial_Properties %>%
group_by(owner,date_of_mortgage,mortgage_amount) %>%
count(tax_district)
datatable(Looking_For_Sort_of_dupulicates)
If you sort by n (which stands for the number of tax districts,) you can see that each purchase goes from 1 to 106 properties, though thankfully most are just 1. This really complicates our analysis, though, because we need the tax district to get the effective rate which is what we will multiply our new value by.
So first, we need to create a proportion for our tax district. A purchase that is in just one tax district will have a multiplier of 1. The rest will be a proportion of the current value.
For example, let’s say we purchased an LLC business for one million dollars (the mortgage) on two parcels of land in two tax districts and it is on the books as being 500,000 dollars total, (the Auditor’s market value.) Parcel A is listed as being worth 100,000 dollars in tax district 1 and Parcel B is listed being worth 400,000 dollars in tax district 2. Then we would have a parcel A multiplier of .2 (or 20 percent of the value) and parcel B having a multiplier of .8 (eighty percent of the value.)
Commercial_Properties <- Commercial_Properties %>%
group_by(owner,date_of_mortgage,mortgage_amount) %>%
mutate(Auditors_Total_Value_of_Parcels =sum(market_value)) %>% #This creates a total for each transaction
ungroup() %>%
mutate(Share_of_Market_Value = market_value/Auditors_Total_Value_of_Parcels) #This creates the share of the value
#Some parcels are valued at Zero, and therefore, dividing by 0 gives you NA. Which screws up our calculations further on. So for the 173 NAs here we will just set them to zero
Commercial_Properties$Share_of_Market_Value[is.na(Commercial_Properties$Share_of_Market_Value)] <- 0
Okay now we are looking for outliers.
In the Athens apartment case that is our example, (https://realestate.cleveland.com/realestate-news/2018/08/big_big_money_legal_loopholes.html) the mortgage was for so much more than the assessed value because it hadn’t be sold in like ~20 years. Every year the state takes the property’s past value, and estimates what someone would want to buy it for, given how many bedrooms and square footage and the neighborhood and whatnot. But really, the value of something is how much someone else wants to pay for it. When the missing tax value is because it was an LLC transfer, that’s what we are looking for.
Assessors generally try to assess a property at about 80 to 90 percent of its “true value” to allow for property prices to ebb and flow a bit. So in our case, we are going to see if a mortgage is 20 percent more than its listed value.
#First we need to set the NAs in mortgage amount to zero or we get an error further down when we are calulating things from it
Commercial_Properties$mortgage_amount[is.na(Commercial_Properties$mortgage_amount)] <- 0
Commercial_Properties$mortgage_amount <- replace(Commercial_Properties$mortgage_amount, Commercial_Properties$mortgage_amount == -999.99, 0)
#Creating a column that shows what twenty percent more is
Commercial_Properties <- Commercial_Properties %>%
group_by(owner,date_of_mortgage,mortgage_amount) %>%
mutate(Twenty_Percent_More = (Auditors_Total_Value_of_Parcels+.2*Auditors_Total_Value_of_Parcels)) %>%
#Creating a column that shows if the mortgage value is twenty percent more than the property value. If a bank is willing to bet a significant amount more thanthe property value, then that is tax dollars lost.
mutate(Is_This_A_Potential_Outlier = ifelse(mortgage_amount > Twenty_Percent_More, "TRUE", "FALSE" )) %>%
ungroup()
Now this is the part where we want to certify that all our numbers are correct.
For example, in Warren County parcel numbers 15045020040, 16365020010, 16365020011, 12205020010, 12355020010, 11065020010, and 12145020020 have a mortgage of 2.55 million but their auditor’s “market value” is only ~125K. Why would a bank give a railroad 2.55 million dollars for property only worth $125K? They wouldn’t.
If you look at the recorder’s info, this is a mortgage for not just Warren county but the whole railroad up from Cincy to Columbus And fixtures, too. Probably trains in this case.
So here we export the data, create a “verified” column, and re-import it.
#so Uncomment this when you want to export
#rio::export(Commercial_Properties, "Warren County Properties to Verify 7-3-19.csv")
This gets exported, then a human looks at the biggest outliers to see if they are true, or if they are like the example above. Then the file is re-imported. You can skip the data verification part if this doesn’t apply.
#Uncomment this ro run it when you import
#Commercial_Properties <- rio::import("Warren County Properties Last Verified 7-21-19.csv")
Now we want to take the mortgage amount as the market amount, assuming we have verified it.
So this says, if this is a potential outlier, and we have verifed that the outlier is true, then take the mortgage amount as the value. If not take the market value.
Commercial_Properties <- Commercial_Properties %>%
mutate(Mortgage_Share=mortgage_amount*Share_of_Market_Value) %>%
mutate(True_Value=ifelse(Is_This_A_Potential_Outlier==TRUE & Verified!="RED", Mortgage_Share, market_value))
How much COULD the county raise if we knew the value of LLC businesses?
What was the value of taxable assets before and after finding more value?
scales::dollar(sum(Commercial_Properties$market_value))
## [1] "$2,742,038,190"
Commercial_Properties$True_Value[is.na(Commercial_Properties$True_Value)] <- 0
scales::dollar(sum(Commercial_Properties$True_Value))
## [1] "$48,274,085,806"
What would the new tax rate be compared to the old tax rate?
First, we can’t average the rates, as shown by this very high-tech post-it: Sorry this is badly labeled, which I realized later. But essentially, on the top is the original calculation. There are two parcels worth 100 and 200 dollars. Both are in a district where the tax rate is .5. If we later determine that those parcel are actually worth 200 and 300, then the district is still going to collect 50 and 100 respectively. But the tax rate for each goes down.
We calculated the proportion that each parcel of land is, because sometimes businesses bought parcels that were in multiple tax districts. But to figure out the new tax rate, we will have to determine the amount per district. And then determine the new tax rate per district.
What’s the median property value in each tax district? What do they pay now? how much would they pay if we knew the true value of LLC transfers?
Tax_Rate_Comparison <- Commercial_Properties %>%
group_by(tax_district) %>%
summarize(
Total_Per_District =sum(Amount_Owed_to_County),
New_Taxable_Value=sum(True_Value*.35),
Old_Rate=mean(`Effective Rate`)*.001,
New_Rate=(Total_Per_District/New_Taxable_Value),
Median_Property_Value=median(market_value),
Current_Median_Tax_Bill=(Median_Property_Value*Old_Rate),
Possible_Median_Tax_Bill=Median_Property_Value*New_Rate,
Amount_Overpaid_By_Median_Property=Current_Median_Tax_Bill-Possible_Median_Tax_Bill)
datatable(Tax_Rate_Comparison)
This looks at the summary of what the median overpaid. It ranges from nothing to several thousand dollars. And again that’s just the median. We could enter find any property and figure out what they would pay.
summary(Tax_Rate_Comparison$Amount_Overpaid_By_Median_Property)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 483.3 3385.8 9490.1 7964.3 146732.6