Unit 2: EDA Cleaning

Author

Key

Published

January 22, 2026

How to read keys: a quick note

We will do our best to use different formatting techniques to keep keys readable so that you can get the most out of them.

All replicated questions from the original assignment shell will be written like this.

And all of our additional commentary will be written in plain text.

Packages, source functions, conflicts

This code chunk loads all packages needed for this assignment.

library(tidyverse) # for general data wrangling
library(tidymodels) # for modeling

This code chunk will set up conflict policies to reduce errors associated with function conflicts.

options(conflicts.policy = "depends.ok")

If we wanted to load more packages, we would load them in after specifying our conflicts policy below. Note that we didn’t load the janitor package below. You use its clean_names() function later but you can use that without loading the package by pre-pending the namespace when calling it (i.e., janitor::clean_names()). We load in the kableExtra package below, but you might not use it!

library(kableExtra, exclude = "group_rows")

You may also use the EDA and plotting functions that John shares on his lab_support repo. You can source the scripts that contain those functions directly from Github with the code below (note that you may need to install the devtools package if you haven’t done this previously).

If you customized your own functions, this would also be an appropriate place to load those in!

source("https://github.com/jjcurtin/lab_support/blob/main/fun_eda.R?raw=true")
source("https://github.com/jjcurtin/lab_support/blob/main/fun_plots.R?raw=true")

Set up some other environment settings.

options(tibble.width = Inf, tibble.print_max = Inf)
theme_set(theme_classic())

Read and Setup Dataframe

Read Data

In the chunk below, set the variable path_data to the location of your data files. Make sure you have your iaml project open in RStudio. When you call here::here() it will set your root path to be inside of the iaml folder. Assuming you have a subfolder called homework and a folder within that folder called unit_02, path_data will work as set. If you have some other organization, you will need to modify path_data to reflect that folder structure.

path_data <- "application_assignments/unit_02"

This assignment will use the Ames Housing Prices Dataset (also seen in Unit 2 of the course text).

Read in the ames_raw_class.csv data below.

ames_all <- read_csv(here::here(path_data, "ames_raw_class.csv"),
                     show_col_types = FALSE) |> 
  glimpse()

Rows: 1,955
Columns: 81
$ PID               <chr> "0526301100", "0526350040", "0526351010", "052710501…
$ `MS SubClass`     <chr> "020", "020", "020", "060", "120", "120", "120", "06…
$ `MS Zoning`       <chr> "RL", "RH", "RL", "RL", "RL", "RL", "RL", "RL", "RL"…
$ `Lot Frontage`    <dbl> 141, 80, 81, 74, 41, 43, 39, 60, 75, 63, 85, NA, 47,…
$ `Lot Area`        <dbl> 31770, 11622, 14267, 13830, 4920, 5005, 5389, 7500, …
$ Street            <chr> "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", "Pav…
$ Alley             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `Lot Shape`       <chr> "IR1", "Reg", "IR1", "IR1", "Reg", "IR1", "IR1", "Re…
$ `Land Contour`    <chr> "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "HLS", "Lvl", "Lv…
$ Utilities         <chr> "AllPub", "AllPub", "AllPub", "AllPub", "AllPub", "A…
$ `Lot Config`      <chr> "Corner", "Inside", "Corner", "Inside", "Inside", "I…
$ `Land Slope`      <chr> "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gt…
$ Neighborhood      <chr> "NAmes", "NAmes", "NAmes", "Gilbert", "StoneBr", "St…
$ `Condition 1`     <chr> "Norm", "Feedr", "Norm", "Norm", "Norm", "Norm", "No…
$ `Condition 2`     <chr> "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", "Nor…
$ `Bldg Type`       <chr> "1Fam", "1Fam", "1Fam", "1Fam", "TwnhsE", "TwnhsE", …
$ `House Style`     <chr> "1Story", "1Story", "1Story", "2Story", "1Story", "1…
$ `Overall Qual`    <dbl> 6, 5, 6, 5, 8, 8, 8, 7, 6, 6, 7, 8, 8, 8, 9, 4, 6, 6…
$ `Overall Cond`    <dbl> 5, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 7, 2, 5, 6, 6…
$ `Year Built`      <dbl> 1960, 1961, 1958, 1997, 2001, 1992, 1995, 1999, 1993…
$ `Year Remod/Add`  <dbl> 1960, 1961, 1958, 1998, 2001, 1992, 1996, 1999, 1994…
$ `Roof Style`      <chr> "Hip", "Gable", "Hip", "Gable", "Gable", "Gable", "G…
$ `Roof Matl`       <chr> "CompShg", "CompShg", "CompShg", "CompShg", "CompShg…
$ `Exterior 1st`    <chr> "BrkFace", "VinylSd", "Wd Sdng", "VinylSd", "CemntBd…
$ `Exterior 2nd`    <chr> "Plywood", "VinylSd", "Wd Sdng", "VinylSd", "CmentBd…
$ `Mas Vnr Type`    <chr> "Stone", "None", "BrkFace", "None", "None", "None", …
$ `Mas Vnr Area`    <dbl> 112, 0, 108, 0, 0, 0, 0, 0, 0, 0, 0, 0, 603, 0, 350,…
$ `Exter Qual`      <chr> "TA", "TA", "TA", "TA", "Gd", "Gd", "Gd", "TA", "TA"…
$ `Exter Cond`      <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA"…
$ Foundation        <chr> "CBlock", "CBlock", "CBlock", "PConc", "PConc", "PCo…
$ `Bsmt Qual`       <chr> "TA", "TA", "TA", "Gd", "Gd", "Gd", "Gd", "TA", "Gd"…
$ `Bsmt Cond`       <chr> "Gd", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA"…
$ `Bsmt Exposure`   <chr> "Gd", "No", "No", "No", "Mn", "No", "No", "No", "No"…
$ `BsmtFin Type 1`  <chr> "BLQ", "Rec", "ALQ", "GLQ", "GLQ", "ALQ", "GLQ", "Un…
$ `BsmtFin SF 1`    <dbl> 639, 468, 923, 791, 616, 263, 1180, 0, 0, 0, 637, 36…
$ `BsmtFin Type 2`  <chr> "Unf", "LwQ", "Unf", "Unf", "Unf", "Unf", "Unf", "Un…
$ `BsmtFin SF 2`    <dbl> 0, 144, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1120, 0, 0, 0, 0,…
$ `Bsmt Unf SF`     <dbl> 441, 270, 406, 137, 722, 1017, 415, 994, 763, 789, 6…
$ `Total Bsmt SF`   <dbl> 1080, 882, 1329, 928, 1338, 1280, 1595, 994, 763, 78…
$ Heating           <chr> "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", "Gas…
$ `Heating QC`      <chr> "Fa", "TA", "TA", "Gd", "Ex", "Ex", "Ex", "Gd", "Gd"…
$ `Central Air`     <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y…
$ Electrical        <chr> "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr"…
$ `1st Flr SF`      <dbl> 1656, 896, 1329, 928, 1338, 1280, 1616, 1028, 763, 7…
$ `2nd Flr SF`      <dbl> 0, 0, 0, 701, 0, 0, 0, 776, 892, 676, 0, 0, 1589, 67…
$ `Low Qual Fin SF` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ `Gr Liv Area`     <dbl> 1656, 896, 1329, 1629, 1338, 1280, 1616, 1804, 1655,…
$ `Bsmt Full Bath`  <dbl> 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0…
$ `Bsmt Half Bath`  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ `Full Bath`       <dbl> 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 3, 2, 1, 1, 2, 2…
$ `Half Bath`       <dbl> 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0…
$ `Bedroom AbvGr`   <dbl> 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 2, 1, 4, 4, 1, 2, 3, 3…
$ `Kitchen AbvGr`   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ `Kitchen Qual`    <chr> "TA", "TA", "Gd", "TA", "Gd", "Gd", "Gd", "Gd", "TA"…
$ `TotRms AbvGrd`   <dbl> 7, 5, 6, 6, 6, 5, 5, 7, 7, 7, 5, 4, 12, 8, 8, 4, 7, …
$ Functional        <chr> "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Ty…
$ Fireplaces        <dbl> 2, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 2, 1…
$ `Fireplace Qu`    <chr> "Gd", NA, NA, "TA", NA, NA, "TA", "TA", "TA", "Gd", …
$ `Garage Type`     <chr> "Attchd", "Attchd", "Attchd", "Attchd", "Attchd", "A…
$ `Garage Yr Blt`   <dbl> 1960, 1961, 1958, 1997, 2001, 1992, 1995, 1999, 1993…
$ `Garage Finish`   <chr> "Fin", "Unf", "Unf", "Fin", "Fin", "RFn", "RFn", "Fi…
$ `Garage Cars`     <dbl> 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2…
$ `Garage Area`     <dbl> 528, 730, 312, 482, 582, 506, 608, 442, 440, 393, 50…
$ `Garage Qual`     <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA"…
$ `Garage Cond`     <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA"…
$ `Paved Drive`     <chr> "P", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y…
$ `Wood Deck SF`    <dbl> 210, 140, 393, 212, 0, 0, 237, 140, 157, 0, 192, 0, …
$ `Open Porch SF`   <dbl> 62, 0, 36, 34, 0, 82, 152, 60, 84, 75, 0, 54, 36, 12…
$ `Enclosed Porch`  <dbl> 0, 0, 0, 0, 170, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `3Ssn Porch`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ `Screen Porch`    <dbl> 0, 120, 0, 0, 0, 144, 0, 0, 0, 0, 0, 140, 210, 0, 0,…
$ `Pool Area`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ `Pool QC`         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Fence             <chr> NA, "MnPrv", NA, "MnPrv", NA, NA, NA, NA, NA, NA, NA…
$ `Misc Feature`    <chr> NA, NA, "Gar2", NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `Misc Val`        <dbl> 0, 0, 12500, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ `Mo Sold`         <dbl> 5, 6, 6, 3, 4, 1, 3, 6, 4, 5, 2, 6, 6, 6, 6, 6, 2, 1…
$ `Yr Sold`         <dbl> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010…
$ `Sale Type`       <chr> "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD"…
$ `Sale Condition`  <chr> "Normal", "Normal", "Normal", "Normal", "Normal", "N…
$ SalePrice         <dbl> 215000, 105000, 172000, 189900, 213500, 191500, 2365…

Select variables

We explore a different set of variables than those demoed in the course text. select() from the dataset for the variables below and convert all variable names to “snake_case”: SalePrice, Garage Area, Neighborhood, MS SubClass, Total Bsmt SF, Bsmt Qual, Central Air, TotRms AbvGrd, Fireplaces, and Fireplace Qu.

For this step, you need to use select() to pull out the relevant columns and then convert all variable names to snake_case using clean_names().

Notice use of back-ticks in the following code chunk for non-standard variable names (i.e., names that aren’t machine readable due to having a space in them).

ames_all <- ames_all |> 
  select(SalePrice,
         `Garage Area`, 
         `Neighborhood`, 
         `MS SubClass`,
         `Total Bsmt SF`, 
         `Bsmt Qual`,
         `Central Air`,
         `TotRms AbvGrd`,
         `Fireplaces`,
         `Fireplace Qu`) |> 
  janitor::clean_names("snake") |> 
  mutate(across(where(is.character), factor)) |>
  glimpse()

Rows: 1,955
Columns: 10
$ sale_price      <dbl> 215000, 105000, 172000, 189900, 213500, 191500, 236500…
$ garage_area     <dbl> 528, 730, 312, 482, 582, 506, 608, 442, 440, 393, 506,…
$ neighborhood    <fct> NAmes, NAmes, NAmes, Gilbert, StoneBr, StoneBr, StoneB…
$ ms_sub_class    <fct> 020, 020, 020, 060, 120, 120, 120, 060, 060, 060, 020,…
$ total_bsmt_sf   <dbl> 1080, 882, 1329, 928, 1338, 1280, 1595, 994, 763, 789,…
$ bsmt_qual       <fct> TA, TA, TA, Gd, Gd, Gd, Gd, TA, Gd, Gd, Gd, Gd, Gd, Gd…
$ central_air     <fct> Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, …
$ tot_rms_abv_grd <dbl> 7, 5, 6, 6, 6, 5, 5, 7, 7, 7, 5, 4, 12, 8, 8, 4, 7, 7,…
$ fireplaces      <dbl> 2, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 2, 1, …
$ fireplace_qu    <fct> Gd, NA, NA, TA, NA, NA, TA, TA, TA, Gd, Po, NA, Gd, NA…

Review the data dictionary

Familiarize yourself with the variables we use above by looking each one up in the data dictionary downloaded with your homework files. Reference the codebook frequently as you perform cleaning checks below.

Exploring the data for cleaning

This script should only contain EDA steps necessary for cleaning the full dataset (i.e., not subsets of it that will be allocated for train, validation, or test). Be mindful of which aspects of the data set you explore at this stage to prevent information leakage between later training and validation sets (to be saved out as the final step of cleaning in this document). Provide observations at each stage of the process, even if you do not make any changes.

Remember that you can use glimpse(), skim_some(), print(), head(), and/or kable tables to explore and display your data. Everything that you need to do has been demonstrated on the course website.

Variable classes

Confirm that all variables are read in as the expected class. Remember that we class nominal and ordinal variables as factors and interval and ratio variables as numeric. Below the code chunk, type some observations you have about the observed variable classes compared to descriptions in the data dictionary. Make any appropriate adjustments to variable class below using mutate(), factor(), or as.numeric().

We already did this above by using glimpse() when we selected our variables, but we replicate it below using skim_some().

ames_all |> 
  skim_some()

Data summary
Name	ames_all
Number of rows	1955
Number of columns	10
_______________________
Column type frequency:
factor	5
numeric	5
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
neighborhood	0	1.00	FALSE	28	NAm: 299, Col: 174, Old: 161, Edw: 135
ms_sub_class	0	1.00	FALSE	16	020: 730, 060: 388, 050: 208, 120: 122
bsmt_qual	57	0.97	FALSE	4	TA: 861, Gd: 808, Ex: 167, Fa: 62
central_air	0	1.00	FALSE	2	Y: 1821, N: 134
fireplace_qu	960	0.51	FALSE	5	Gd: 481, TA: 407, Fa: 44, Po: 33

Variable type: numeric

skim_variable	n_missing	complete_rate	p0	p100
sale_price	0	1	12789	745000
garage_area	1	1	0	1488
total_bsmt_sf	1	1	0	6110
tot_rms_abv_grd	0	1	3	14
fireplaces	0	1	0	3

Variable class notes: All variables are either of type factor or of type numeric. We will think more carefully about the factors, etc. in the eda_modeling script. The classes as they were read in match the codebook. Though ms_sub_class is a character variable containing numeric values, the codebook tells us that these numeric codes are nominal values representing different classes of dwellings, indicating that this variable is best kept as factor.

Missing data

Use an appropriate technique to review missing data at this stage. Clearly document missing data (“missingness”) across each variable in the dataset. For variables with high missingness, write code that allows you to visually inspect all observations of missing data (for each such variable). Using information from related variables and the data dictionary to guide you, speculate (in prose) why data may be missing. Clean variables with high missingness using mutate(), replace_na(), and fct_relevel() if you believe any of the NAs are not really missing but instead problems with how the data were coded (e.g., see the example of this in the book.)

bsmt_qual and fireplace_qu have the greatest number of missing variables, so we’ll go ahead and explore these two further. garage_area and total_bsmt_sf are only missing one observation, so we won’t explore them further here.

ames_all |> 
  skim_some() |> 
  select(skim_variable, n_missing, complete_rate) # view missing variables

# A tibble: 10 × 3
   skim_variable   n_missing complete_rate
   <chr>               <int>         <dbl>
 1 neighborhood            0         1    
 2 ms_sub_class            0         1    
 3 bsmt_qual              57         0.971
 4 central_air             0         1    
 5 fireplace_qu          960         0.509
 6 sale_price              0         1    
 7 garage_area             1         0.999
 8 total_bsmt_sf           1         0.999
 9 tot_rms_abv_grd         0         1    
10 fireplaces              0         1

ames_all |> filter(is.na(bsmt_qual)) |>
  print_kbl()

sale_price	garage_area	neighborhood	ms_sub_class	total_bsmt_sf	bsmt_qual	central_air	tot_rms_abv_grd	fireplaces	fireplace_qu
112000	539	Sawyer	090	0	NA	Y	8	0	NA
55000	0	IDOTRR	190	0	NA	N	7	0	NA
84900	240	Edwards	030	0	NA	N	5	0	NA
100000	0	Edwards	090	0	NA	N	8	0	NA
160000	612	SawyerW	090	0	NA	Y	8	2	TA
102900	616	NAmes	020	0	NA	Y	7	0	NA
92900	400	NAmes	090	0	NA	N	6	0	NA
45000	308	OldTown	030	0	NA	N	4	0	NA
139600	452	Edwards	020	0	NA	Y	8	1	Gd
122000	498	Edwards	020	0	NA	Y	6	0	NA
84000	290	Edwards	020	0	NA	N	5	0	NA
131000	576	SawyerW	070	0	NA	Y	7	1	TA
118964	410	SawyerW	090	0	NA	Y	8	0	NA
147983	720	SawyerW	090	0	NA	Y	8	0	NA
118858	400	SawyerW	090	0	NA	Y	8	0	NA
142953	528	SawyerW	090	0	NA	Y	12	0	NA
113722	400	SawyerW	090	0	NA	Y	8	0	NA
198500	672	Edwards	085	0	NA	Y	7	1	Ex
98000	240	Edwards	050	0	NA	N	9	0	NA
82000	528	Edwards	020	0	NA	N	4	0	NA
57625	280	IDOTRR	030	0	NA	N	4	0	NA
126000	539	Mitchel	090	0	NA	Y	8	0	NA
72500	287	BrkSide	020	0	NA	N	4	0	NA
89900	390	OldTown	020	0	NA	N	3	0	NA
106250	0	OldTown	190	0	NA	N	7	0	NA
98300	420	NAmes	020	0	NA	Y	7	0	NA
97000	308	OldTown	020	0	NA	Y	4	0	NA
79000	280	BrkSide	020	NA	NA	Y	4	0	NA
81300	164	BrkSide	030	0	NA	N	6	0	NA
134432	680	ClearCr	050	0	NA	Y	7	1	Gd
82000	0	Edwards	090	0	NA	N	6	0	NA
82000	0	Edwards	090	0	NA	N	6	0	NA
82500	0	Edwards	090	0	NA	N	6	0	NA
96000	0	Edwards	050	0	NA	Y	8	0	NA
13100	487	IDOTRR	020	0	NA	N	4	0	NA
100000	484	Mitchel	090	0	NA	Y	8	0	NA
106500	294	NAmes	020	0	NA	Y	5	0	NA
101800	462	NAmes	090	0	NA	Y	6	0	NA
99000	300	NAmes	020	0	NA	Y	7	0	NA
104500	331	OldTown	020	0	NA	Y	5	0	NA
89500	460	Sawyer	020	0	NA	N	5	0	NA
141000	579	Edwards	090	0	NA	Y	11	0	NA
153500	400	Crawfor	020	0	NA	Y	7	1	Gd
68104	256	IDOTRR	050	0	NA	Y	4	0	NA
130000	1041	Mitchel	020	0	NA	Y	9	0	NA
169000	672	Gilbert	060	0	NA	Y	7	1	TA
157500	484	NAmes	020	0	NA	Y	5	1	Gd
109500	625	NAmes	020	0	NA	Y	5	0	NA
109900	420	NAmes	020	0	NA	Y	4	0	NA
81400	400	NAmes	090	0	NA	N	6	0	NA
87500	400	NAmes	090	0	NA	N	6	0	NA
80500	0	BrkSide	050	0	NA	N	3	0	NA
131000	394	Sawyer	020	0	NA	Y	7	1	Gd
125000	260	Edwards	030	0	NA	N	6	1	TA
135000	569	Edwards	020	0	NA	Y	7	1	Ex
230000	312	GrnHill	120	0	NA	Y	5	0	NA
81500	270	Mitchel	020	0	NA	Y	6	0	NA

ames_all |> filter(is.na(fireplace_qu)) |> 
  print_kbl()

sale_price	garage_area	neighborhood	ms_sub_class	total_bsmt_sf	bsmt_qual	central_air	tot_rms_abv_grd	fireplace_qu
105000	730	NAmes	020	882	TA	Y	5	NA
172000	312	NAmes	020	1329	TA	Y	6	NA
213500	582	StoneBr	120	1338	Gd	Y	6	NA
191500	506	StoneBr	120	1280	Gd	Y	5	NA
212000	528	StoneBr	120	1488	Gd	Y	4	NA
164000	492	Gilbert	050	559	Gd	Y	8	NA
141000	400	Gilbert	020	864	TA	Y	4	NA
216000	663	Somerst	060	814	Gd	Y	7	NA
126000	525	NAmes	020	882	TA	Y	4	NA
105500	320	BrDale	160	525	TA	Y	6	NA
120000	308	NPkVill	120	836	Gd	Y	4	NA
290941	868	NridgHt	020	1544	Gd	Y	7	NA
175500	474	NridgHt	160	764	Gd	Y	6	NA
160000	437	Blmngtn	120	1145	Gd	Y	6	NA
221000	486	NoRidge	060	1195	Gd	Y	7	NA
204500	676	Somerst	020	1218	Gd	Y	4	NA
233000	618	SawyerW	060	956	Gd	Y	8	NA
181000	484	SawyerW	060	831	Gd	Y	8	NA
125000	264	SawyerW	120	744	Gd	Y	4	NA
194500	457	SawyerW	060	996	Gd	Y	7	NA
152000	480	SawyerW	020	1040	Gd	Y	6	NA
67500	429	SawyerW	030	816	TA	N	5	NA
112000	539	Sawyer	090	0	NA	Y	8	NA
148000	576	Sawyer	085	1109	TA	Y	6	NA
122000	280	Sawyer	020	882	TA	Y	5	NA
127000	260	Sawyer	020	1040	TA	Y	6	NA
190000	588	Somerst	160	840	Gd	Y	3	NA
151000	480	Somerst	160	600	Gd	Y	4	NA
149500	480	Somerst	160	600	Gd	Y	4	NA
177500	440	Somerst	160	756	Gd	Y	5	NA
218500	535	NWAmes	020	1829	TA	Y	8	NA
171000	441	NWAmes	060	715	TA	Y	7	NA
142250	490	NAmes	020	1232	TA	Y	6	NA
128950	0	NAmes	020	950	TA	Y	6	NA
159000	504	NAmes	020	1209	Gd	Y	6	NA
178900	517	NAmes	020	1510	TA	Y	6	NA
136300	480	NAmes	080	533	TA	Y	8	NA
137500	480	NAmes	085	936	TA	Y	6	NA
84900	0	NAmes	090	1728	TA	Y	10	NA
116500	0	BrkSide	020	858	TA	Y	4	NA
76500	0	BrkSide	030	728	TA	Y	6	NA
128000	280	NAmes	020	1056	TA	Y	6	NA
154300	480	NAmes	090	1105	TA	Y	12	NA
135000	576	NAmes	090	1604	TA	Y	8	NA
136000	308	NAmes	020	1143	TA	Y	6	NA
145000	294	NAmes	020	1314	TA	Y	5	NA
148000	312	NAmes	020	1194	TA	Y	6	NA
142000	531	NAmes	020	1188	TA	Y	6	NA
108538	312	NAmes	020	1206	TA	Y	6	NA
135000	318	NAmes	020	864	TA	Y	4	NA
122500	305	NAmes	020	972	TA	Y	5	NA
105000	676	NAmes	030	576	TA	Y	4	NA
144900	490	NAmes	020	1086	TA	Y	6	NA
129000	308	NAmes	020	936	TA	Y	4	NA
144000	576	OldTown	090	1056	TA	Y	4	NA
132000	280	NAmes	050	816	TA	Y	5	NA
154000	305	NAmes	020	1246	TA	Y	6	NA
134800	576	NAmes	020	900	TA	Y	5	NA
148000	484	NAmes	020	1175	TA	Y	6	NA
143000	551	OldTown	020	1347	TA	Y	6	NA
107400	379	OldTown	050	840	Fa	N	8	NA
119000	240	OldTown	045	827	Gd	Y	6	NA
100000	315	OldTown	050	1008	TA	Y	6	NA
105900	484	OldTown	030	624	TA	Y	5	NA
139000	416	OldTown	050	686	Fa	Y	7	NA
240000	624	OldTown	070	346	Fa	Y	7	NA
76500	0	OldTown	190	840	TA	N	12	NA
149700	923	OldTown	020	912	TA	Y	6	NA
140750	624	OldTown	070	738	Fa	Y	6	NA
128500	363	BrkSide	050	988	TA	Y	7	NA
134000	240	BrkSide	050	608	TA	Y	5	NA
139900	312	BrkSide	050	780	TA	Y	6	NA
123900	624	BrkSide	050	528	TA	Y	5	NA
159900	0	OldTown	070	780	TA	Y	7	NA
122000	180	OldTown	070	662	TA	Y	6	NA
110000	516	IDOTRR	090	816	Gd	N	5	NA
55000	0	IDOTRR	190	0	NA	N	7	NA
107500	180	ClearCr	050	689	Gd	N	6	NA
95000	231	SWISU	050	600	Fa	N	5	NA
93369	0	SWISU	070	707	TA	N	7	NA
94000	0	Sawyer	050	780	TA	N	6	NA
136000	0	Sawyer	090	1832	TA	N	8	NA
158000	299	Sawyer	060	663	TA	Y	7	NA
85000	440	Sawyer	030	660	Fa	N	4	NA
128000	436	Sawyer	020	1067	Gd	Y	4	NA
83000	366	Edwards	030	458	TA	N	5	NA
129000	596	Edwards	020	1144	TA	Y	6	NA
114000	504	Edwards	020	1250	TA	Y	5	NA
147000	564	SawyerW	085	936	Gd	Y	5	NA
245350	776	CollgCr	020	1694	Gd	Y	7	NA
206000	632	CollgCr	020	1187	Gd	Y	6	NA
198900	740	CollgCr	020	1226	Gd	Y	6	NA
187000	615	CollgCr	020	1222	Gd	Y	6	NA
200500	523	CollgCr	060	804	Gd	Y	7	NA
150000	308	CollgCr	020	1026	Gd	Y	5	NA
161750	299	CollgCr	060	689	Gd	Y	6	NA
127000	484	CollgCr	020	876	TA	Y	5	NA
224900	598	CollgCr	020	1649	Gd	Y	6	NA
213000	606	CollgCr	020	1473	Gd	Y	7	NA
84900	240	Edwards	030	0	NA	N	5	NA
155891	319	Edwards	020	381	Fa	Y	6	NA
100000	0	Edwards	090	0	NA	N	8	NA
144000	336	Edwards	020	1196	Gd	Y	6	NA
90000	0	Edwards	020	960	TA	Y	5	NA
140000	440	Edwards	020	1121	Gd	Y	5	NA
80000	280	Edwards	190	672	Gd	N	5	NA
58500	200	Edwards	030	864	TA	N	5	NA
215000	484	Crawfor	090	1032	TA	Y	11	NA
203135	473	Crawfor	080	423	Gd	Y	6	NA
82000	200	IDOTRR	030	480	TA	Y	4	NA
68400	528	IDOTRR	050	698	TA	Y	6	NA
102776	384	IDOTRR	050	859	TA	Y	7	NA
55993	504	IDOTRR	020	540	TA	N	4	NA
50138	330	IDOTRR	030	756	TA	Y	5	NA
201000	573	Mitchel	020	1560	Ex	Y	7	NA
170000	776	Mitchel	090	1958	TA	Y	9	NA
179781	520	Timber	020	1214	Gd	Y	6	NA
174000	400	Timber	060	384	Gd	Y	7	NA
139000	0	Mitchel	090	912	TA	Y	8	NA
83500	286	MeadowV	160	546	TA	Y	6	NA
85000	336	MeadowV	160	536	Gd	Y	4	NA
76000	0	MeadowV	160	546	TA	Y	5	NA
75500	0	MeadowV	180	630	Gd	Y	3	NA
88250	0	MeadowV	160	546	TA	Y	5	NA
85500	286	MeadowV	160	546	TA	Y	5	NA
157900	312	Mitchel	020	1188	TA	Y	6	NA
159000	616	Mitchel	090	1216	TA	Y	10	NA
136000	576	Mitchel	060	624	Gd	Y	6	NA
161000	275	Mitchel	050	864	TA	Y	8	NA
124500	502	NAmes	020	882	Fa	Y	5	NA
240000	495	StoneBr	120	1418	Gd	Y	5	NA
239500	528	StoneBr	120	1587	Gd	Y	6	NA
180500	500	Gilbert	020	1424	Gd	Y	5	NA
136000	0	NAmes	090	896	Gd	Y	8	NA
128000	477	NAmes	020	1180	TA	Y	6	NA
143000	336	NAmes	020	1043	Gd	Y	6	NA
120500	660	NAmes	020	864	TA	Y	5	NA
124500	463	NAmes	020	864	TA	Y	5	NA
97000	576	NAmes	020	864	TA	Y	4	NA
111000	264	BrDale	160	483	TA	Y	5	NA
112000	280	BrDale	160	630	TA	Y	6	NA
97000	288	BrDale	160	483	TA	Y	5	NA
119500	264	BrDale	160	630	TA	Y	6	NA
100000	288	NAmes	020	892	TA	Y	5	NA
233170	644	NridgHt	020	1502	Gd	Y	7	NA
167000	400	Gilbert	080	384	Gd	Y	7	NA
275000	972	Somerst	020	1696	Gd	Y	7	NA
210000	676	Somerst	060	768	Gd	Y	6	NA
225000	529	Somerst	020	1436	Ex	Y	6	NA
229800	625	Somerst	020	1402	Gd	Y	7	NA
241000	438	Somerst	060	1092	Gd	Y	7	NA
185000	400	Somerst	020	1221	Gd	Y	6	NA
234500	588	Somerst	020	1553	Gd	Y	6	NA
203000	845	SawyerW	020	1512	Gd	Y	6	NA
184900	555	SawyerW	020	1176	Gd	Y	5	NA
159000	576	SawyerW	020	1114	Gd	Y	6	NA
142000	0	SawyerW	020	1114	Gd	Y	6	NA
224243	788	SawyerW	020	1450	Gd	Y	6	NA
171500	528	SawyerW	060	816	Gd	Y	6	NA
145000	542	SawyerW	120	1332	Gd	Y	5	NA
184000	495	SawyerW	060	754	Gd	Y	6	NA
162000	465	SawyerW	060	676	Gd	Y	6	NA
135000	484	Sawyer	020	980	TA	Y	6	NA
141000	732	Sawyer	020	864	TA	Y	5	NA
122000	440	Sawyer	020	864	TA	Y	5	NA
124100	288	Sawyer	020	1024	TA	Y	5	NA
123000	252	Sawyer	020	912	TA	Y	6	NA
164500	528	Somerst	160	612	Gd	Y	4	NA
172500	440	Somerst	160	729	Gd	Y	6	NA
180000	440	Somerst	160	756	Gd	Y	4	NA
241600	540	Veenker	020	1740	Gd	Y	7	NA
154000	441	NWAmes	060	727	TA	Y	8	NA
113000	336	NWAmes	020	1216	TA	Y	5	NA
227000	528	NWAmes	060	810	Gd	Y	6	NA
130000	312	NAmes	020	864	TA	Y	5	NA
143000	440	NAmes	020	1568	TA	Y	7	NA
118000	308	NAmes	020	864	TA	Y	5	NA
167000	472	NAmes	020	1516	TA	Y	6	NA
124500	270	NAmes	020	1041	TA	Y	6	NA
82500	0	BrkSide	030	420	TA	Y	5	NA
175000	550	NAmes	020	1680	TA	Y	5	NA
128900	338	NAmes	020	1050	TA	Y	5	NA
140000	271	NAmes	050	856	TA	Y	6	NA
124000	792	NAmes	090	1584	TA	Y	8	NA
150000	286	NAmes	020	1169	TA	Y	6	NA
155000	297	NAmes	020	1215	TA	Y	6	NA
120000	304	NAmes	020	1202	TA	Y	6	NA
153000	350	NAmes	020	1382	TA	Y	6	NA
131000	297	NAmes	020	1062	TA	Y	6	NA
123000	270	NAmes	020	608	TA	Y	5	NA
145500	230	NAmes	020	1031	TA	Y	5	NA
102900	616	NAmes	020	0	NA	Y	7	NA
95000	420	OldTown	190	978	Fa	Y	7	NA
129900	246	NAmes	020	1200	TA	Y	6	NA
99900	366	OldTown	020	792	TA	Y	4	NA
135000	180	OldTown	050	660	Fa	Y	8	NA
109500	240	OldTown	030	768	TA	Y	5	NA
105000	338	OldTown	050	840	TA	Y	6	NA
78500	320	OldTown	030	630	TA	Y	4	NA
190000	907	NAmes	090	1248	TA	Y	12	NA
154000	364	NAmes	020	1380	TA	Y	6	NA
200000	968	NAmes	090	1248	TA	Y	12	NA
143500	480	NAmes	020	951	TA	Y	6	NA
135000	308	NAmes	020	1105	TA	Y	5	NA
153000	756	NAmes	020	992	TA	Y	5	NA
92900	400	NAmes	090	0	NA	N	6	NA
128500	900	NAmes	050	795	TA	N	5	NA
138000	240	NAmes	050	1208	TA	Y	7	NA
128000	294	NAmes	020	1041	TA	Y	6	NA
139000	294	NAmes	020	1029	TA	Y	6	NA
138000	288	NAmes	080	528	Gd	Y	6	NA
132500	672	NAmes	020	1148	TA	Y	6	NA
133500	384	NAmes	050	832	TA	Y	6	NA
135000	576	NAmes	050	864	TA	Y	6	NA
144750	483	NAmes	060	780	TA	Y	8	NA
109500	308	NAmes	020	894	TA	N	5	NA
110000	440	OldTown	050	440	TA	Y	5	NA
128900	162	OldTown	050	901	TA	Y	6	NA
130000	472	OldTown	050	240	TA	Y	6	NA
129000	0	OldTown	070	504	TA	Y	7	NA
94550	0	OldTown	190	600	Fa	N	8	NA
124500	400	OldTown	090	960	TA	Y	10	NA
103000	0	OldTown	050	801	Fa	N	6	NA
129500	308	OldTown	050	768	TA	Y	6	NA
93000	256	OldTown	190	811	TA	Y	7	NA
80000	288	OldTown	030	861	Fa	Y	5	NA
45000	308	OldTown	030	0	NA	N	4	NA
37900	0	OldTown	050	600	TA	N	6	NA
99500	287	OldTown	030	624	Fa	Y	5	NA
113000	0	OldTown	190	736	TA	Y	8	NA
110000	240	OldTown	050	720	TA	Y	5	NA
160000	357	OldTown	070	917	Fa	Y	8	NA
124900	456	OldTown	050	1088	TA	Y	7	NA
146500	640	OldTown	070	741	TA	Y	8	NA
34900	0	IDOTRR	030	720	TA	N	4	NA
128000	240	BrkSide	050	672	TA	Y	6	NA
119000	180	BrkSide	045	884	TA	Y	4	NA
100000	440	BrkSide	030	1145	TA	Y	5	NA
141500	576	BrkSide	050	927	TA	Y	5	NA
133000	240	BrkSide	050	884	TA	Y	6	NA
105000	180	BrkSide	030	715	TA	Y	5	NA
115000	264	BrkSide	030	960	TA	Y	5	NA
214500	441	BrkSide	075	844	TA	N	10	NA
155000	576	OldTown	070	1048	TA	Y	8	NA
179900	216	OldTown	070	672	TA	Y	8	NA
62500	0	OldTown	090	1040	TA	N	11	NA
103000	539	IDOTRR	030	780	TA	Y	5	NA
97500	0	OldTown	190	1020	TA	N	9	NA
70000	0	IDOTRR	085	858	Gd	Y	5	NA
179000	0	Edwards	090	1200	Gd	Y	5	NA
179000	0	Edwards	090	1200	Gd	Y	5	NA
61000	0	Edwards	030	683	Fa	N	4	NA
63000	226	Edwards	050	585	TA	N	6	NA
139000	480	Sawyer	020	1121	TA	Y	5	NA
135000	284	Edwards	020	1134	TA	Y	6	NA
82500	539	Edwards	030	861	TA	Y	4	NA
122000	498	Edwards	020	0	NA	Y	6	NA
84000	290	Edwards	020	0	NA	N	5	NA
139500	266	Edwards	120	1049	Gd	Y	5	NA
105000	280	Edwards	050	560	TA	Y	6	NA
118964	410	SawyerW	090	0	NA	Y	8	NA
147983	720	SawyerW	090	0	NA	Y	8	NA
118858	400	SawyerW	090	0	NA	Y	8	NA
142953	528	SawyerW	090	0	NA	Y	12	NA
113722	400	SawyerW	090	0	NA	Y	8	NA
239000	702	CollgCr	020	1800	Ex	Y	7	NA
221800	810	CollgCr	020	1254	Gd	Y	5	NA
194500	632	CollgCr	020	1232	Gd	Y	6	NA
152000	576	CollgCr	020	1040	Gd	Y	6	NA
138000	480	CollgCr	020	990	Gd	Y	5	NA
197900	546	CollgCr	060	866	Gd	Y	6	NA
204000	577	CollgCr	020	1490	Gd	Y	6	NA
192000	666	CollgCr	020	1431	Gd	Y	6	NA
195000	493	CollgCr	060	831	Gd	Y	7	NA
227000	544	CollgCr	020	1573	Gd	Y	6	NA
230000	622	CollgCr	060	944	Gd	Y	6	NA
187100	605	CollgCr	020	1413	Gd	Y	6	NA
203000	577	CollgCr	020	1431	Gd	Y	6	NA
134900	444	CollgCr	020	914	TA	Y	5	NA
150500	484	CollgCr	020	1040	Gd	Y	5	NA
136500	336	CollgCr	020	864	TA	Y	6	NA
133900	396	CollgCr	085	768	Gd	Y	5	NA
133000	396	CollgCr	085	768	Gd	Y	5	NA
250000	1231	CollgCr	020	1569	Gd	Y	6	NA
198500	550	CollgCr	060	840	Gd	Y	6	NA
211000	608	CollgCr	060	944	Gd	Y	6	NA
219500	645	CollgCr	060	1057	Gd	Y	8	NA
178000	573	CollgCr	020	1212	Gd	Y	6	NA
140000	420	CollgCr	120	848	Gd	Y	4	NA
190000	572	CollgCr	060	864	Gd	Y	7	NA
190000	570	CollgCr	020	1500	Gd	Y	6	NA
167000	480	Edwards	020	1486	TA	Y	7	NA
145500	525	Edwards	180	547	Gd	Y	5	NA
118000	0	Edwards	020	698	TA	Y	4	NA
85000	320	Edwards	020	876	TA	Y	5	NA
120000	195	Edwards	020	1078	Fa	Y	6	NA
98000	240	Edwards	050	0	NA	N	9	NA
99900	0	Edwards	030	864	Fa	Y	4	NA
82000	528	Edwards	020	0	NA	N	4	NA
119900	0	Edwards	090	1678	TA	Y	10	NA
110000	528	Edwards	050	684	TA	Y	7	NA
117000	660	Edwards	050	984	TA	Y	6	NA
159434	281	SWISU	050	793	TA	Y	6	NA
60000	246	SWISU	030	290	TA	N	3	NA
155000	576	SWISU	070	560	Gd	Y	6	NA
163500	432	Crawfor	030	876	Gd	Y	5	NA
147000	468	IDOTRR	070	672	TA	Y	6	NA
123000	576	IDOTRR	070	760	TA	N	11	NA
78000	189	IDOTRR	070	596	TA	N	6	NA
75000	200	IDOTRR	030	572	TA	N	4	NA
57625	280	IDOTRR	030	0	NA	N	4	NA
126000	539	Mitchel	090	0	NA	Y	8	NA
152000	672	Mitchel	020	1582	Gd	Y	5	NA
224500	650	Timber	060	846	Ex	Y	6	NA
170000	486	Timber	060	912	Gd	Y	9	NA
139500	384	Mitchel	020	1008	TA	Y	6	NA
130000	288	Mitchel	020	864	TA	Y	5	NA
80000	286	MeadowV	180	630	Gd	Y	3	NA
88000	286	MeadowV	160	546	TA	Y	6	NA
131900	336	Mitchel	085	796	Gd	Y	4	NA
112000	0	Mitchel	020	833	TA	Y	5	NA
143000	480	Mitchel	050	796	Gd	Y	5	NA
130000	440	Mitchel	060	624	Gd	Y	6	NA
177900	404	Gilbert	020	1348	Gd	Y	7	NA
180000	506	StoneBr	120	1280	Gd	Y	5	NA
181900	598	NWAmes	020	1260	Gd	Y	6	NA
175000	540	NWAmes	020	1126	Gd	Y	6	NA
133000	480	NAmes	090	896	Gd	Y	8	NA
151000	460	NAmes	020	1054	Gd	Y	6	NA
111900	288	NAmes	020	936	Gd	Y	4	NA
123000	576	NAmes	020	864	Gd	Y	5	NA
103400	264	BrDale	160	483	TA	Y	5	NA
100000	264	BrDale	160	483	TA	Y	5	NA
100500	352	BrDale	160	483	TA	Y	5	NA
106000	280	BrDale	160	483	TA	Y	5	NA
89500	264	BrDale	160	483	TA	Y	5	NA
111750	264	BrDale	160	630	TA	Y	6	NA
140000	440	NPkVill	120	1069	Gd	Y	4	NA
143000	460	NPkVill	160	855	Gd	Y	7	NA
110000	264	NAmes	020	864	TA	Y	5	NA
317500	905	NridgHt	020	1582	Ex	Y	7	NA
155000	474	NridgHt	160	764	Gd	Y	6	NA
154000	474	NridgHt	160	764	Gd	Y	6	NA
190000	400	Gilbert	080	856	Gd	Y	5	NA
176000	400	Gilbert	060	728	Gd	Y	8	NA
227680	554	Somerst	020	1417	Gd	Y	6	NA
212700	588	Somerst	020	1363	Gd	Y	6	NA
250580	529	Somerst	020	1372	Ex	Y	6	NA
182000	480	Somerst	020	1428	Gd	Y	6	NA
226700	603	Somerst	060	866	Gd	Y	7	NA
205950	562	Somerst	060	813	Gd	Y	7	NA
207500	673	Somerst	060	864	Gd	Y	7	NA
141000	0	SawyerW	020	1080	Gd	Y	6	NA
159000	576	SawyerW	020	1114	TA	Y	6	NA
155000	576	SawyerW	020	1114	Gd	Y	6	NA
173000	520	SawyerW	060	798	Gd	Y	6	NA
170000	483	SawyerW	020	1162	Gd	Y	6	NA
182000	525	SawyerW	060	754	Gd	Y	6	NA
163000	474	SawyerW	060	886	Gd	Y	7	NA
190500	627	SawyerW	060	827	Gd	Y	7	NA
179200	496	SawyerW	020	1278	Gd	Y	6	NA
153900	530	SawyerW	120	1199	Gd	Y	5	NA
144000	352	Sawyer	020	912	TA	Y	4	NA
119916	276	Sawyer	020	864	Gd	Y	5	NA
196000	530	Somerst	120	1391	Gd	Y	5	NA
171900	625	Somerst	160	625	Gd	Y	5	NA
178000	495	Somerst	120	1235	Gd	Y	5	NA
146000	540	Somerst	160	689	Gd	Y	5	NA
172900	440	Somerst	160	744	Gd	Y	6	NA
170000	462	Somerst	160	672	Gd	Y	5	NA
200000	480	Somerst	160	960	Gd	Y	7	NA
162500	516	Veenker	020	1232	Gd	Y	6	NA
183000	516	NWAmes	080	1427	TA	Y	7	NA
145000	528	NWAmes	020	1258	TA	Y	5	NA
140500	484	NWAmes	090	1625	TA	Y	8	NA
141000	470	NAmes	060	732	TA	Y	7	NA
147000	576	NAmes	020	912	TA	Y	5	NA
135000	576	NAmes	020	912	TA	Y	5	NA
142600	576	NAmes	090	1728	TA	Y	10	NA
135000	506	NWAmes	090	1656	TA	Y	8	NA
170000	464	NWAmes	060	825	Gd	Y	7	NA
173000	484	NWAmes	060	800	TA	Y	7	NA
178400	588	NAmes	085	944	TA	Y	6	NA
109008	352	NAmes	020	907	TA	Y	5	NA
155000	525	NAmes	020	1120	TA	Y	6	NA
174900	490	NAmes	080	1127	TA	Y	6	NA
145000	504	NAmes	020	1092	Gd	Y	6	NA
168500	520	NAmes	080	1248	TA	Y	6	NA
140000	451	NAmes	020	1114	TA	Y	5	NA
142000	528	NAmes	020	1252	TA	Y	7	NA
153000	264	NAmes	020	1118	TA	Y	6	NA
72500	287	BrkSide	020	0	NA	N	4	NA
87000	231	BrkSide	070	348	TA	Y	5	NA
141500	450	NAmes	020	1296	TA	Y	6	NA
119000	288	NAmes	050	1032	TA	Y	6	NA
112900	312	NAmes	020	1036	TA	Y	5	NA
124000	336	NAmes	020	1144	TA	Y	6	NA
140000	506	NAmes	020	931	TA	Y	6	NA
136000	288	NAmes	020	1080	TA	Y	5	NA
133000	384	NAmes	020	1104	TA	Y	5	NA
116000	240	NAmes	020	720	TA	Y	4	NA
137500	636	NAmes	020	1152	TA	Y	6	NA
130000	400	NAmes	020	984	TA	N	5	NA
89900	390	OldTown	020	0	NA	N	3	NA
114000	288	OldTown	050	747	TA	Y	5	NA
86900	308	OldTown	030	672	TA	Y	4	NA
106250	0	OldTown	190	0	NA	N	7	NA
98300	420	NAmes	020	0	NA	Y	7	NA
159000	311	NAmes	020	1256	TA	Y	6	NA
125900	299	NAmes	020	1027	TA	Y	6	NA
138000	576	NAmes	050	768	TA	Y	5	NA
112500	280	NAmes	045	936	TA	Y	4	NA
105500	528	NAmes	045	832	TA	Y	4	NA
130000	0	NAmes	090	1800	TA	N	10	NA
150000	480	NAmes	050	768	TA	Y	6	NA
109500	350	NAmes	020	825	TA	Y	4	NA
167900	442	NAmes	060	780	TA	Y	7	NA
136870	264	NAmes	020	1117	TA	Y	6	NA
143000	308	OldTown	020	192	TA	N	7	NA
73000	504	OldTown	030	680	Fa	N	4	NA
122600	400	OldTown	050	780	TA	Y	6	NA
111000	330	OldTown	070	680	TA	N	6	NA
64000	256	OldTown	030	672	Fa	Y	4	NA
139500	308	OldTown	075	728	TA	Y	7	NA
200000	484	OldTown	050	1362	Gd	Y	6	NA
119164	440	OldTown	050	801	TA	Y	8	NA
95000	0	OldTown	050	684	Fa	Y	5	NA
115000	250	OldTown	020	789	TA	Y	5	NA
147000	528	OldTown	020	924	TA	Y	6	NA
112500	288	OldTown	020	928	TA	Y	5	NA
107900	281	OldTown	020	901	TA	Y	4	NA
65000	0	OldTown	020	448	Fa	Y	6	NA
98000	513	OldTown	070	624	TA	N	7	NA
114000	308	OldTown	050	346	TA	Y	5	NA
129400	0	OldTown	070	572	Fa	Y	7	NA
55000	400	OldTown	070	723	Fa	N	5	NA
131500	352	OldTown	050	1050	TA	N	7	NA
97000	308	OldTown	020	0	NA	Y	4	NA
115500	308	OldTown	020	869	TA	Y	5	NA
108000	252	BrkSide	050	672	TA	Y	8	NA
124000	240	BrkSide	050	672	TA	Y	6	NA
106900	200	BrkSide	070	554	TA	Y	6	NA
79000	280	BrkSide	020	NA	NA	Y	4	NA
81300	164	BrkSide	030	0	NA	N	6	NA
68500	240	BrkSide	030	520	TA	N	4	NA
145000	240	BrkSide	050	883	TA	Y	8	NA
130000	275	BrkSide	030	816	TA	Y	5	NA
160000	360	OldTown	060	1242	TA	Y	8	NA
127500	228	OldTown	050	796	Gd	Y	7	NA
120000	400	IDOTRR	050	880	TA	Y	6	NA
127500	320	IDOTRR	030	1040	TA	Y	5	NA
89500	0	IDOTRR	050	319	TA	Y	6	NA
79900	0	OldTown	050	901	TA	Y	7	NA
85000	576	IDOTRR	030	451	Fa	Y	5	NA
82375	0	IDOTRR	190	536	TA	N	8	NA
124000	240	Sawyer	020	1046	TA	N	6	NA
127500	0	Edwards	190	644	TA	Y	6	NA
148000	440	Sawyer	085	981	Gd	Y	6	NA
129500	384	Sawyer	020	1040	TA	Y	5	NA
130000	450	Sawyer	020	894	TA	Y	5	NA
152000	506	Sawyer	085	1060	Gd	Y	5	NA
108959	400	Sawyer	090	1198	Gd	Y	6	NA
95541	400	Sawyer	190	1300	TA	Y	6	NA
80000	0	Sawyer	020	1006	TA	Y	5	NA
149350	271	Sawyer	020	1228	TA	Y	6	NA
144900	300	Sawyer	020	960	TA	Y	6	NA
140000	300	Sawyer	080	533	TA	Y	6	NA
99600	0	Edwards	090	864	Gd	N	4	NA
134500	280	Edwards	060	621	TA	Y	7	NA
107500	0	Edwards	190	1117	Gd	Y	5	NA
125000	240	Edwards	190	1164	TA	N	5	NA
79000	0	Edwards	050	572	TA	N	5	NA
200000	603	ClearCr	060	616	Gd	Y	8	NA
155000	528	CollgCr	020	990	Gd	Y	5	NA
145000	672	CollgCr	020	990	Gd	Y	5	NA
215700	600	CollgCr	060	864	Gd	Y	6	NA
207500	532	CollgCr	060	782	Gd	Y	6	NA
188000	527	CollgCr	060	880	Gd	Y	8	NA
191000	577	CollgCr	020	1468	Gd	Y	6	NA
235000	626	CollgCr	060	928	Gd	Y	6	NA
130250	308	CollgCr	020	938	Gd	Y	5	NA
147000	576	CollgCr	080	940	Gd	Y	5	NA
110000	280	CollgCr	020	924	Gd	Y	5	NA
127000	352	CollgCr	020	864	TA	Y	5	NA
124900	352	CollgCr	020	780	TA	Y	4	NA
224900	543	CollgCr	060	884	Gd	Y	8	NA
136500	420	CollgCr	120	848	Gd	Y	3	NA
145000	420	CollgCr	120	848	Gd	Y	3	NA
185000	721	CollgCr	090	1838	Gd	Y	8	NA
208500	548	CollgCr	060	856	Gd	Y	8	NA
186500	470	CollgCr	020	1445	Gd	Y	6	NA
210000	484	CollgCr	020	1436	Gd	Y	8	NA
179900	484	CollgCr	020	1234	Ex	Y	7	NA
218836	814	CollgCr	020	1564	Gd	Y	6	NA
85000	0	Edwards	030	796	Fa	Y	4	NA
145900	0	Edwards	090	1272	TA	Y	9	NA
147500	276	Edwards	020	1256	TA	Y	6	NA
148000	525	Edwards	180	547	Gd	Y	5	NA
82000	0	Edwards	090	0	NA	N	6	NA
82000	0	Edwards	090	0	NA	N	6	NA
118000	0	Edwards	090	1440	TA	N	8	NA
82500	0	Edwards	090	0	NA	N	6	NA
91900	0	Edwards	190	784	TA	N	4	NA
120000	0	Edwards	050	585	TA	Y	5	NA
96000	0	Edwards	050	0	NA	Y	8	NA
130500	240	Edwards	050	768	TA	Y	6	NA
135000	548	Edwards	050	941	TA	Y	7	NA
135500	300	Edwards	050	816	TA	Y	8	NA
100000	312	Edwards	080	416	Ex	Y	6	NA
108000	205	Edwards	030	949	TA	Y	6	NA
98000	0	Edwards	030	864	TA	N	5	NA
67000	0	Edwards	030	864	TA	N	5	NA
135900	0	SWISU	190	780	TA	N	9	NA
140000	400	SWISU	070	672	TA	N	7	NA
189000	621	SWISU	070	612	TA	Y	8	NA
177000	308	Crawfor	070	840	Gd	Y	8	NA
115000	240	SWISU	050	1064	TA	Y	6	NA
110000	225	Crawfor	050	884	TA	Y	6	NA
120000	240	IDOTRR	050	720	Fa	Y	5	NA
119000	0	IDOTRR	050	780	Gd	Y	7	NA
99500	360	IDOTRR	030	520	TA	Y	5	NA
13100	487	IDOTRR	020	0	NA	N	4	NA
40000	250	IDOTRR	070	649	TA	N	6	NA
81000	1248	IDOTRR	030	894	TA	Y	6	NA
65000	216	IDOTRR	070	592	Fa	Y	5	NA
300000	786	Mitchel	020	1840	Ex	Y	7	NA
100000	484	Mitchel	090	0	NA	Y	8	NA
294000	788	Timber	020	1518	Ex	Y	8	NA
167500	400	Timber	080	384	Gd	Y	7	NA
218689	666	Timber	060	796	Gd	Y	6	NA
195000	435	Timber	020	1666	Ex	Y	6	NA
206300	784	Mitchel	090	1344	Gd	Y	8	NA
160500	392	Mitchel	120	1189	Ex	Y	4	NA
124000	499	MeadowV	120	1040	Gd	Y	6	NA
105000	297	MeadowV	180	526	Gd	Y	5	NA
81000	0	MeadowV	180	630	Gd	Y	3	NA
128500	264	Mitchel	020	816	TA	Y	4	NA
119500	264	Mitchel	020	876	TA	Y	5	NA
138000	816	Mitchel	020	816	TA	Y	4	NA
134500	264	Mitchel	020	845	TA	Y	6	NA
160000	423	Mitchel	050	983	Gd	Y	7	NA
137500	484	Mitchel	020	864	TA	Y	6	NA
103000	288	IDOTRR	070	686	TA	Y	7	NA
177000	388	Gilbert	060	794	Gd	Y	6	NA
251000	499	StoneBr	120	1494	Gd	Y	6	NA
178750	420	Gilbert	060	691	Gd	Y	6	NA
160000	420	Gilbert	020	1168	Gd	Y	6	NA
170000	461	StoneBr	120	1074	Gd	Y	5	NA
182000	480	StoneBr	120	1166	Gd	Y	5	NA
155000	828	Gilbert	190	1100	Gd	Y	7	NA
174000	480	NWAmes	020	1284	Gd	Y	5	NA
151500	495	NWAmes	020	1153	Gd	Y	6	NA
152000	462	NWAmes	060	780	TA	Y	7	NA
139000	288	NAmes	080	372	TA	Y	5	NA
184000	484	NAmes	060	725	TA	Y	7	NA
155000	576	NAmes	020	925	TA	Y	7	NA
119500	280	NAmes	020	950	TA	Y	5	NA
110000	684	NAmes	020	858	TA	Y	4	NA
128000	270	NAmes	020	914	TA	Y	4	NA
133000	367	NAmes	120	723	Gd	Y	4	NA
113500	264	BrDale	160	525	TA	Y	6	NA
113000	264	BrDale	160	672	TA	Y	7	NA
122500	440	BrDale	160	765	TA	Y	6	NA
142500	319	NPkVill	120	1061	Gd	Y	4	NA
129250	360	NAmes	020	988	TA	Y	5	NA
232698	554	NridgHt	020	1721	Gd	Y	7	NA
250000	876	NridgHt	060	1298	Gd	Y	7	NA
181755	572	Gilbert	060	768	Gd	Y	7	NA
168165	454	Gilbert	060	608	Gd	Y	6	NA
203000	388	Gilbert	020	1326	Gd	Y	6	NA
176485	436	Gilbert	020	1302	Gd	Y	6	NA
181134	396	Gilbert	020	1340	Gd	Y	6	NA
166000	440	Gilbert	060	752	Gd	Y	6	NA
177594	400	Gilbert	060	728	Gd	Y	7	NA
173500	462	Gilbert	020	1246	Gd	Y	6	NA
188500	437	Gilbert	080	835	Gd	Y	5	NA
162000	400	Gilbert	060	660	Gd	Y	6	NA
213133	605	Somerst	020	1369	Gd	Y	5	NA
260000	484	Somerst	020	1538	Gd	Y	7	NA
225000	474	Somerst	020	1496	Gd	Y	7	NA
250000	574	Somerst	060	982	Gd	Y	7	NA
208900	598	Somerst	020	1338	Gd	Y	6	NA
255000	590	Somerst	060	896	Gd	Y	8	NA
212109	561	Somerst	060	725	Gd	Y	8	NA
249700	826	Somerst	020	1656	Gd	Y	7	NA
146000	0	SawyerW	020	1100	Gd	Y	6	NA
201000	471	SawyerW	060	928	Gd	Y	7	NA
144000	504	SawyerW	085	840	Gd	Y	5	NA
120750	308	Sawyer	020	894	Gd	Y	5	NA
132500	264	Sawyer	020	999	TA	Y	6	NA
129000	544	Sawyer	020	1040	TA	Y	5	NA
145000	686	Sawyer	020	1040	TA	Y	5	NA
128500	572	Sawyer	020	988	TA	Y	5	NA
129000	431	Sawyer	020	1037	Fa	Y	5	NA
166000	484	Somerst	120	1141	Gd	Y	5	NA
207500	528	Somerst	120	1550	Gd	Y	5	NA
177000	440	Somerst	160	756	Gd	Y	4	NA
204000	625	Somerst	020	1339	Gd	Y	5	NA
143450	412	NWAmes	020	912	TA	Y	7	NA
164000	528	NWAmes	020	1295	Gd	Y	6	NA
127000	311	NWAmes	020	1052	TA	Y	6	NA
127000	308	NAmes	020	949	TA	Y	5	NA
126500	461	NAmes	020	1065	TA	Y	6	NA
145000	264	NAmes	080	588	TA	Y	6	NA
120000	297	NAmes	020	912	TA	Y	5	NA
106500	294	NAmes	020	0	NA	Y	5	NA
117600	312	NAmes	020	894	TA	Y	5	NA
161000	440	NAmes	080	1042	TA	Y	5	NA
150750	624	NAmes	020	1063	TA	Y	7	NA
101800	462	NAmes	090	0	NA	Y	6	NA
138500	512	NAmes	045	822	TA	Y	4	NA
64000	0	BrkSide	050	370	TA	N	4	NA
144000	288	NAmes	020	1078	TA	Y	6	NA
126000	792	NAmes	090	1560	TA	Y	8	NA
114500	275	NAmes	020	912	TA	Y	5	NA
144000	315	NAmes	050	874	TA	Y	7	NA
139400	260	NAmes	020	1056	TA	Y	6	NA
116000	308	NAmes	020	922	TA	Y	5	NA
135000	286	NAmes	020	1048	TA	Y	6	NA
142000	336	NAmes	020	864	TA	Y	6	NA
128600	484	NAmes	090	1560	TA	Y	8	NA
125000	410	NAmes	020	948	TA	Y	5	NA
134500	240	NAmes	020	1008	TA	Y	6	NA
127000	252	NAmes	020	925	TA	Y	5	NA
132000	252	NAmes	020	928	Gd	Y	4	NA
301600	1200	NAmes	070	666	TA	Y	9	NA
109000	240	OldTown	050	735	TA	Y	5	NA
103200	0	OldTown	045	882	TA	Y	4	NA
128500	580	OldTown	050	1151	TA	Y	6	NA
148000	0	OldTown	070	736	Gd	Y	6	NA
120000	240	NAmes	020	920	TA	Y	5	NA
128500	288	NAmes	020	952	TA	Y	5	NA
135000	254	NAmes	020	1010	TA	Y	6	NA
99000	300	NAmes	020	0	NA	Y	7	NA
125500	294	NAmes	020	864	TA	Y	5	NA
132000	360	OldTown	070	264	Fa	Y	7	NA
127500	576	OldTown	050	845	TA	Y	6	NA
140000	672	OldTown	090	936	Gd	Y	4	NA
120000	308	OldTown	050	649	TA	Y	6	NA
89471	672	OldTown	030	907	Fa	Y	7	NA
85000	252	OldTown	050	570	Fa	N	6	NA
108500	308	OldTown	030	960	TA	Y	5	NA
110500	0	OldTown	030	686	TA	Y	4	NA
100000	480	OldTown	030	968	TA	Y	5	NA
184900	816	OldTown	070	725	TA	N	7	NA
114000	480	OldTown	070	616	TA	Y	6	NA
90000	560	OldTown	190	662	TA	N	10	NA
144100	468	OldTown	050	926	Gd	Y	6	NA
117500	205	OldTown	070	1008	TA	Y	8	NA
152000	216	OldTown	040	1149	TA	Y	5	NA
153500	812	OldTown	030	1559	TA	Y	5	NA
104500	331	OldTown	020	0	NA	Y	5	NA
128250	308	BrkSide	050	910	Fa	Y	6	NA
135000	224	BrkSide	050	728	TA	Y	6	NA
132000	280	BrkSide	050	672	TA	Y	7	NA
132500	379	BrkSide	050	756	TA	Y	6	NA
137500	180	BrkSide	070	939	TA	Y	8	NA
165000	576	BrkSide	050	768	TA	Y	7	NA
90000	576	BrkSide	050	750	Fa	Y	7	NA
153575	384	BrkSide	070	698	TA	Y	7	NA
117000	216	OldTown	050	969	TA	N	6	NA
127000	0	IDOTRR	070	684	TA	Y	7	NA
101000	288	IDOTRR	075	530	TA	N	6	NA
50000	264	IDOTRR	050	771	TA	Y	6	NA
126000	240	Edwards	050	756	TA	N	7	NA
130000	308	Sawyer	190	925	Gd	Y	6	NA
115400	440	Sawyer	020	1005	TA	Y	5	NA
118500	261	Sawyer	020	1029	TA	Y	5	NA
123000	264	Sawyer	020	1040	TA	Y	6	NA
125000	384	Sawyer	020	894	TA	Y	5	NA
108000	300	Sawyer	020	948	TA	Y	6	NA
119900	576	Sawyer	190	768	Gd	Y	6	NA
115000	264	Sawyer	020	876	TA	Y	5	NA
134500	528	Sawyer	020	943	TA	Y	5	NA
127000	386	Sawyer	020	955	TA	Y	6	NA
89500	460	Sawyer	020	0	NA	N	5	NA
109900	336	Edwards	020	484	TA	N	4	NA
154000	332	Edwards	050	988	TA	Y	6	NA
118000	399	SWISU	020	864	TA	Y	6	NA
150000	0	Edwards	090	1440	Gd	Y	8	NA
86000	0	Edwards	030	528	TA	Y	5	NA
130000	264	Edwards	120	1038	TA	Y	5	NA
125000	256	Edwards	020	1342	Fa	Y	7	NA
96000	280	Edwards	050	951	TA	N	7	NA
360000	894	CollgCr	020	2140	Gd	Y	8	NA
203000	508	CollgCr	020	1274	Gd	Y	6	NA
195400	628	CollgCr	020	1208	Gd	Y	6	NA
217000	796	CollgCr	020	1546	Gd	Y	7	NA
191000	578	CollgCr	060	879	Gd	Y	7	NA
187500	444	ClearCr	060	832	TA	Y	9	NA
139500	747	CollgCr	020	1022	Gd	Y	6	NA
146000	384	CollgCr	020	990	Gd	Y	5	NA
123600	0	CollgCr	020	990	Gd	Y	5	NA
226000	558	CollgCr	020	1453	Gd	Y	7	NA
180000	500	CollgCr	060	840	Gd	Y	8	NA
134900	748	CollgCr	020	1040	Gd	Y	5	NA
120000	440	CollgCr	020	864	TA	Y	5	NA
122000	440	CollgCr	020	969	TA	Y	5	NA
127000	576	CollgCr	020	864	TA	Y	5	NA
200000	542	CollgCr	060	860	Gd	Y	7	NA
131500	420	CollgCr	120	848	Gd	Y	3	NA
185000	531	CollgCr	020	1337	Gd	Y	6	NA
179400	552	CollgCr	060	738	Gd	Y	7	NA
213500	639	CollgCr	060	939	Gd	Y	8	NA
179600	626	CollgCr	020	1422	Gd	Y	7	NA
173500	578	CollgCr	020	1169	Gd	Y	5	NA
167000	400	CollgCr	060	768	Gd	Y	6	NA
110000	520	Edwards	020	1088	TA	Y	4	NA
110000	622	Edwards	020	1179	TA	Y	5	NA
141000	579	Edwards	090	0	NA	Y	11	NA
157000	450	Edwards	020	1090	Ex	Y	5	NA
171500	489	Edwards	060	734	Gd	Y	6	NA
140000	525	Edwards	180	547	Gd	Y	5	NA
131750	392	Edwards	020	960	TA	Y	4	NA
111000	248	Edwards	020	864	TA	Y	5	NA
98500	0	Edwards	090	1195	TA	N	8	NA
79000	0	Edwards	030	544	TA	Y	6	NA
112000	384	Edwards	050	560	Fa	N	6	NA
79275	528	Edwards	020	864	TA	Y	5	NA
200000	0	SWISU	190	1440	TA	Y	14	NA
150000	0	SWISU	090	1296	TA	N	12	NA
96500	200	Crawfor	045	768	TA	N	5	NA
136500	352	Crawfor	030	1290	TA	Y	6	NA
145000	492	Crawfor	090	979	Gd	N	8	NA
140000	528	Crawfor	190	728	TA	Y	5	NA
170000	628	Crawfor	020	1680	Gd	Y	7	NA
200000	509	Blueste	120	1004	Gd	Y	4	NA
115000	0	IDOTRR	050	793	TA	N	7	NA
141000	720	IDOTRR	050	720	TA	Y	6	NA
87000	360	IDOTRR	045	720	TA	N	5	NA
150909	NA	IDOTRR	070	859	Gd	Y	6	NA
67000	338	IDOTRR	030	961	Fa	Y	6	NA
152000	282	IDOTRR	050	910	TA	Y	6	NA
68104	256	IDOTRR	050	0	NA	Y	4	NA
119600	0	IDOTRR	190	957	TA	N	9	NA
140000	720	IDOTRR	020	1073	TA	Y	6	NA
134000	484	Mitchel	020	1084	TA	Y	5	NA
148000	360	Mitchel	080	852	TA	Y	4	NA
231713	433	Timber	020	1689	Gd	Y	7	NA
174000	528	Timber	020	1265	Gd	Y	6	NA
180500	471	Timber	020	1267	Ex	Y	5	NA
147000	0	Mitchel	020	1120	Gd	Y	6	NA
130000	1041	Mitchel	020	0	NA	Y	9	NA
132250	648	Mitchel	020	1064	TA	Y	5	NA
143000	576	Mitchel	090	1632	TA	Y	8	NA
85500	253	MeadowV	160	546	TA	Y	5	NA
93900	0	MeadowV	160	546	TA	Y	5	NA
75000	286	MeadowV	160	546	TA	Y	6	NA
84500	0	MeadowV	180	630	Gd	Y	3	NA
80000	444	Mitchel	020	1396	TA	N	6	NA
129500	360	Mitchel	020	845	TA	Y	4	NA
135000	502	Mitchel	020	1090	Gd	Y	6	NA
124500	264	Mitchel	020	816	TA	Y	5	NA
139000	264	Mitchel	020	816	Gd	Y	4	NA
149900	452	NAmes	020	1178	TA	Y	5	NA
270000	618	StoneBr	160	2062	Gd	Y	9	NA
145000	572	Gilbert	190	1056	TA	N	5	NA
202500	440	NWAmes	020	1208	Gd	Y	8	NA
229000	786	NWAmes	050	1204	Gd	Y	9	NA
153500	495	NWAmes	020	1056	Gd	Y	6	NA
155000	462	NWAmes	020	1103	TA	Y	6	NA
138800	299	NAmes	020	1096	TA	Y	6	NA
116050	280	NAmes	020	864	TA	Y	5	NA
97500	576	NAmes	020	864	TA	Y	5	NA
190000	543	NAmes	120	1487	Gd	Y	4	NA
105500	440	BrDale	160	525	TA	Y	6	NA
116000	264	BrDale	160	494	TA	Y	6	NA
118000	264	BrDale	160	672	TA	Y	7	NA
89000	264	BrDale	160	483	TA	Y	5	NA
137500	319	NPkVill	120	1055	TA	Y	4	NA
342643	840	NridgHt	060	1249	Ex	Y	9	NA
174190	427	Gilbert	020	1326	Gd	Y	6	NA
185101	440	Gilbert	060	738	Gd	Y	8	NA
156820	440	Blmngtn	120	1142	Gd	Y	6	NA
287602	617	Gilbert	060	1035	Gd	Y	8	NA
199000	510	Somerst	020	1504	Gd	Y	7	NA
186500	615	Somerst	060	756	Gd	Y	6	NA
200825	615	Somerst	060	858	Gd	Y	7	NA
197000	614	Somerst	060	770	Gd	Y	6	NA
139500	0	SawyerW	020	1142	TA	Y	5	NA
186800	612	SawyerW	020	1400	Gd	Y	6	NA
132000	0	SawyerW	020	1131	Gd	Y	6	NA
142500	484	SawyerW	020	1141	Gd	Y	6	NA
158000	576	SawyerW	020	1158	Gd	Y	5	NA
184000	462	SawyerW	060	891	Gd	Y	8	NA
184900	449	SawyerW	060	784	Gd	Y	7	NA
175900	484	SawyerW	060	761	Gd	Y	7	NA
248500	462	SawyerW	060	836	Gd	Y	7	NA
151000	484	SawyerW	120	1217	Gd	Y	5	NA
150000	472	SawyerW	060	636	Gd	Y	6	NA
107000	384	SawyerW	020	1056	TA	Y	5	NA
119900	300	SawyerW	020	828	TA	Y	6	NA
129500	624	Sawyer	020	988	TA	Y	6	NA
125000	504	Sawyer	090	1728	TA	Y	10	NA
116000	294	Sawyer	020	882	TA	Y	5	NA
133500	484	Sawyer	020	925	TA	Y	5	NA
137000	288	Sawyer	020	935	TA	Y	5	NA
151000	480	Somerst	160	600	Gd	Y	4	NA
147400	480	Somerst	160	600	Gd	Y	4	NA
149900	480	Somerst	160	600	Gd	Y	4	NA
144152	480	Somerst	160	600	Gd	Y	4	NA
166000	440	Somerst	160	756	Gd	Y	4	NA
165000	490	Somerst	160	673	Gd	Y	6	NA
164500	477	NWAmes	020	1334	TA	Y	7	NA
140000	528	NWAmes	020	1164	TA	Y	6	NA
155000	530	NAmes	060	900	Gd	Y	7	NA
138500	288	NAmes	020	1052	TA	Y	5	NA
124400	264	NAmes	020	1051	TA	Y	6	NA
158000	568	NAmes	080	1141	TA	Y	6	NA
146000	884	NAmes	020	864	TA	Y	6	NA
136500	368	NAmes	020	1050	TA	Y	6	NA
145000	588	NAmes	090	1626	TA	Y	8	NA
140000	264	NAmes	190	588	TA	Y	6	NA
150000	750	NAmes	020	1008	TA	Y	6	NA
151500	286	NAmes	020	1248	TA	Y	6	NA
85500	0	BrkSide	030	440	Gd	Y	4	NA
79900	528	BrkSide	030	600	Fa	Y	5	NA
127000	576	BrkSide	050	504	Fa	Y	5	NA
135000	280	NAmes	020	1056	TA	Y	7	NA
121000	240	NAmes	020	936	TA	Y	5	NA
98600	480	NAmes	020	1235	TA	Y	6	NA
118000	286	NAmes	020	936	TA	Y	5	NA
132500	540	NAmes	090	1106	TA	Y	6	NA
156000	286	NAmes	020	1050	TA	Y	6	NA
139500	280	NAmes	020	1008	TA	Y	4	NA
127000	684	NAmes	080	301	Gd	Y	6	NA
142000	270	NAmes	020	884	TA	Y	4	NA
127000	649	NAmes	050	784	TA	Y	6	NA
112000	398	OldTown	050	617	Fa	Y	6	NA
99800	371	OldTown	030	713	TA	Y	5	NA
117000	326	OldTown	020	833	TA	Y	4	NA
108480	326	OldTown	030	1073	TA	Y	4	NA
68000	660	NAmes	030	715	TA	N	4	NA
86900	280	OldTown	050	448	Fa	Y	5	NA
105000	936	OldTown	050	672	TA	Y	6	NA
147000	264	NAmes	020	1135	TA	Y	6	NA
115000	288	NAmes	020	912	TA	Y	5	NA
120000	288	NAmes	020	864	TA	Y	4	NA
129900	308	NAmes	020	816	TA	Y	5	NA
147500	597	NAmes	050	910	TA	Y	6	NA
135000	0	NAmes	050	720	TA	N	6	NA
109500	625	NAmes	020	0	NA	Y	5	NA
109900	420	NAmes	020	0	NA	Y	4	NA
81400	400	NAmes	090	0	NA	N	6	NA
87500	400	NAmes	090	0	NA	N	6	NA
119000	0	NAmes	090	1556	TA	Y	8	NA
141000	300	NAmes	020	1150	TA	Y	6	NA
146000	288	NAmes	020	1150	TA	Y	6	NA
134500	300	NAmes	085	925	TA	Y	6	NA
105000	336	NAmes	020	864	TA	Y	5	NA
124000	0	NAmes	190	1025	TA	Y	6	NA
107000	210	OldTown	070	686	TA	N	8	NA
135000	240	OldTown	050	1212	TA	Y	6	NA
156500	400	OldTown	050	780	TA	Y	7	NA
139900	576	OldTown	070	712	TA	Y	7	NA
111500	576	OldTown	190	938	TA	N	9	NA
108000	280	OldTown	030	1032	TA	N	5	NA
105000	528	OldTown	020	576	TA	Y	5	NA
116000	440	OldTown	050	648	Fa	Y	6	NA
96900	216	OldTown	070	738	TA	Y	7	NA
135500	576	OldTown	190	960	TA	Y	5	NA
139000	450	OldTown	050	816	TA	Y	7	NA
61500	200	OldTown	030	492	Fa	Y	3	NA
64000	250	OldTown	030	798	TA	Y	5	NA
137000	440	OldTown	190	1020	Fa	N	9	NA
87000	779	OldTown	070	1095	TA	N	8	NA
155000	432	OldTown	075	1226	TA	Y	9	NA
79000	352	OldTown	050	540	TA	N	6	NA
114504	216	OldTown	070	684	TA	N	7	NA
157000	576	OldTown	070	596	Fa	Y	8	NA
125000	312	OldTown	030	1200	TA	Y	7	NA
125000	624	OldTown	020	960	TA	Y	5	NA
64500	0	IDOTRR	050	1020	TA	N	5	NA
88000	450	BrkSide	030	768	TA	N	6	NA
80500	0	BrkSide	050	0	NA	N	3	NA
110000	180	BrkSide	050	741	TA	Y	7	NA
117000	255	OldTown	050	485	TA	Y	5	NA
116500	390	IDOTRR	070	396	Fa	Y	7	NA
72000	0	IDOTRR	030	610	TA	N	4	NA
150000	576	Sawyer	090	1921	TA	Y	8	NA
130000	288	ClearCr	080	1062	TA	Y	6	NA
80000	180	SWISU	030	830	TA	Y	4	NA
157000	246	SWISU	020	1073	Gd	Y	4	NA
154500	264	Sawyer	020	1059	TA	Y	6	NA
156500	580	Sawyer	090	1967	Gd	Y	10	NA
157000	586	Sawyer	090	1949	TA	Y	10	NA
145000	364	Sawyer	080	648	TA	Y	3	NA
130000	576	Sawyer	085	782	Gd	Y	4	NA
148400	450	ClearCr	150	430	Gd	Y	7	NA
136900	325	Edwards	050	994	TA	Y	7	NA
149900	305	Edwards	050	1000	TA	Y	7	NA
123500	308	Edwards	020	980	Ex	Y	6	NA
224500	920	SawyerW	090	1344	Gd	Y	8	NA
183600	632	CollgCr	020	1208	Gd	Y	6	NA
221500	865	CollgCr	020	1614	Gd	Y	7	NA
204900	675	CollgCr	020	1498	Gd	Y	5	NA
239799	868	CollgCr	060	1391	Gd	Y	7	NA
181000	610	CollgCr	060	784	Gd	Y	6	NA
148000	576	CollgCr	085	870	Gd	Y	5	NA
193000	591	CollgCr	020	1425	Gd	Y	5	NA
217000	576	CollgCr	020	1479	Gd	Y	6	NA
214000	472	CollgCr	020	1588	Gd	Y	6	NA
196000	515	CollgCr	060	842	Gd	Y	7	NA
200000	492	CollgCr	060	916	Gd	Y	8	NA
157500	317	CollgCr	060	675	Gd	Y	6	NA
174000	672	CollgCr	080	995	TA	Y	6	NA
119900	297	CollgCr	020	864	TA	Y	5	NA
137000	484	Landmrk	160	630	Gd	Y	5	NA
222000	591	CollgCr	060	826	Gd	Y	6	NA
197000	529	CollgCr	060	872	Gd	Y	8	NA
230000	900	CollgCr	020	1654	Gd	Y	6	NA
207000	545	CollgCr	020	1390	Gd	Y	7	NA
200000	544	CollgCr	020	1682	Gd	Y	7	NA
203160	578	CollgCr	060	851	Gd	Y	7	NA
195800	572	CollgCr	060	784	Gd	Y	6	NA
212900	782	CollgCr	020	1552	Gd	Y	7	NA
196500	565	CollgCr	060	860	Gd	Y	7	NA
198000	502	CollgCr	020	1336	Gd	Y	6	NA
98000	528	Edwards	070	676	Gd	Y	6	NA
102000	396	Edwards	020	836	Fa	Y	6	NA
119900	360	Edwards	020	1176	Gd	Y	5	NA
117250	0	Edwards	020	914	Gd	Y	4	NA
142500	525	Edwards	180	547	Gd	Y	5	NA
134000	380	Edwards	160	970	Gd	Y	7	NA
137500	380	Edwards	160	970	Gd	Y	7	NA
97900	265	Edwards	050	624	Fa	Y	5	NA
92000	528	Edwards	050	832	Fa	Y	7	NA
107000	288	Edwards	050	1093	TA	N	9	NA
81000	0	Edwards	050	658	TA	Y	8	NA
115000	572	Edwards	020	864	TA	Y	5	NA
116000	280	Edwards	050	686	TA	Y	7	NA
128000	440	SWISU	190	780	TA	N	6	NA
102000	0	SWISU	050	1008	TA	Y	7	NA
112000	240	SWISU	050	824	Fa	N	6	NA
102000	215	IDOTRR	030	976	TA	N	5	NA
145400	506	IDOTRR	050	861	TA	Y	6	NA
72000	0	IDOTRR	030	432	Fa	N	4	NA
108000	570	IDOTRR	050	637	TA	Y	7	NA
35311	308	IDOTRR	020	480	TA	N	4	NA
115000	0	IDOTRR	190	660	TA	N	8	NA
78000	0	IDOTRR	050	216	Fa	N	6	NA
209000	490	Mitchel	020	1491	Ex	Y	7	NA
135000	504	Mitchel	090	975	Gd	Y	4	NA
148000	528	Mitchel	080	1128	TA	Y	6	NA
171000	766	Mitchel	090	1907	TA	Y	9	NA
230000	312	GrnHill	120	0	NA	Y	5	NA
250000	693	Timber	060	1216	Gd	Y	8	NA
81500	270	Mitchel	020	0	NA	Y	6	NA
215000	784	Mitchel	090	1288	Gd	Y	8	NA
153500	405	Mitchel	120	1237	Ex	Y	5	NA
126500	288	Mitchel	020	864	TA	Y	5	NA
146500	928	Mitchel	090	1652	TA	Y	8	NA
79400	253	MeadowV	160	546	TA	Y	5	NA
92000	0	MeadowV	180	630	Gd	Y	3	NA
87550	286	MeadowV	160	546	TA	Y	5	NA
90500	0	MeadowV	160	546	TA	Y	5	NA
71000	286	MeadowV	160	546	TA	Y	6	NA
150900	574	Mitchel	090	1728	TA	Y	8	NA
188000	560	Mitchel	090	1728	TA	Y	8	NA
131000	484	Mitchel	020	864	Gd	Y	5	NA

# Convert NAs to "none" where stated in codebook
ames_all <- ames_all |> 
  mutate(bsmt_qual = fct_expand(bsmt_qual, "no_bsmt"), # add a new level to the factor
         bsmt_qual = replace_na(bsmt_qual, "no_bsmt"), # recode NA to that new level
         fireplace_qu = fct_expand(fireplace_qu, "no_fireplace"), 
         fireplace_qu = replace_na(fireplace_qu, "no_fireplace"))

# review order of levels b/c they are ordinal, no fireplace/basement is worst
ames_all$bsmt_qual |> levels()

[1] "Ex"      "Fa"      "Gd"      "TA"      "no_bsmt"

ames_all$fireplace_qu |> levels()

[1] "Ex"           "Fa"           "Gd"           "Po"           "TA"          
[6] "no_fireplace"

bq_levels <- c("no_bsmt", "Po", "Fa", "TA", "Gd", "Ex") 
ames_all <- ames_all |> 
  mutate(bsmt_qual = fct_relevel(bsmt_qual, bq_levels))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `bsmt_qual = fct_relevel(bsmt_qual, bq_levels)`.
Caused by warning:
! 1 unknown level in `f`: Po

# the warning about the missing level "Po" is OK.  No basements were rated poor
ames_all$bsmt_qual |> levels()

[1] "no_bsmt" "Fa"      "TA"      "Gd"      "Ex"

fq_levels <- c("no_fireplace", "Po", "Fa", "TA", "Gd", "Ex")
ames_all <- ames_all |> 
  dplyr::mutate(fireplace_qu = fct_relevel(fireplace_qu, fq_levels)) 

ames_all$fireplace_qu |> levels()

[1] "no_fireplace" "Po"           "Fa"           "TA"           "Gd"          
[6] "Ex"

Missing data notes: Basement and fireplace quality both have high numbers of missing values. The codebook explicitly states that ‘NA’ for these variables represents observations that do not have garages or fireplaces (we can also see that these observations have either garage areas of 0 or 0 fireplaces respectively). These observations may be better represented as “no basement” ” and “no fireplace” than missing (though this may not be best for the ordinal nature of these variables). We can explore that further during modeling EDA

Numeric variables

Explore min and max values for numeric variables, recording notes on any observations that look suspicious or potentially invalid. Use the data dictionary and associated variables to help you decide whether suspicious observations may represent (in)valid responses.

# skim data, looking at numeric min and max values
ames_all |>
  skim_some() |> 
  filter(skim_type == "numeric") |>  # Select only numeric variables since min/max only apply to them
  select(skim_variable, numeric.p0, numeric.p100)

# A tibble: 5 × 3
  skim_variable   numeric.p0 numeric.p100
  <chr>                <dbl>        <dbl>
1 sale_price           12789       745000
2 garage_area              0         1488
3 total_bsmt_sf            0         6110
4 tot_rms_abv_grd          3           14
5 fireplaces               0            3

# 14 rooms above ground isn't impossible, but seems high -- let's take a look!
ames_all |> 
  filter(tot_rms_abv_grd == 14) |>
  print_kbl()

sale_price	garage_area	neighborhood	ms_sub_class	total_bsmt_sf	bsmt_qual	central_air	tot_rms_abv_grd	fireplaces	fireplace_qu
2e+05	0	SWISU	190	1440	TA	Y	14	0	no_fireplace

Numeric variable notes: Numeric values appear to be in the expected range. There were no numeric values coded as factor that made sense to convert to examine min/max values (i.e., ms_sub_class is not ordinal, doesn’t make sense to look at numeric values). I thought 14 rooms above ground seemed like a lot and looked at that one further – the observation with 14 rooms above ground had an ms_sub_class of “190” indicating it is a 2 family conversion home, which makes this number more believable. No changes need to be made at this step because it’s not an obvious error. We can explore it as a possible outlier further in modeling EDA

Categorical variables

Use print() and levels() print the levels of each categorical variable. You might consider using walk() to do all the categorical variables at once! You can use tidy_responses() (a function from John) to convert all responses to snake_case. Check to make sure all levels converted properly. If needed, correct any response levels with conversion errors using mutate() and fct_recode(). Document your observations for categorical levels.

# View all categorical response labels
ames_all |> 
  select(where(is.factor)) |>
  walk(\(column) print(levels(column)))

 [1] "Blmngtn" "Blueste" "BrDale"  "BrkSide" "ClearCr" "CollgCr" "Crawfor"
 [8] "Edwards" "Gilbert" "Greens"  "GrnHill" "IDOTRR"  "Landmrk" "MeadowV"
[15] "Mitchel" "NAmes"   "NoRidge" "NPkVill" "NridgHt" "NWAmes"  "OldTown"
[22] "Sawyer"  "SawyerW" "Somerst" "StoneBr" "SWISU"   "Timber"  "Veenker"
 [1] "020" "030" "040" "045" "050" "060" "070" "075" "080" "085" "090" "120"
[13] "150" "160" "180" "190"
[1] "no_bsmt" "Fa"      "TA"      "Gd"      "Ex"     
[1] "N" "Y"
[1] "no_fireplace" "Po"           "Fa"           "TA"           "Gd"          
[6] "Ex"

# Tidy all character responses except ms_sub_class (since these are numbers and do not need to be tidied)
ames_all <- ames_all |>
  mutate(across(where(is.factor) & !all_of("ms_sub_class"), tidy_responses))

# Check response labels
ames_all |> 
  select(where(is.factor)) |>
  walk(\(column) print(levels(column)))

 [1] "blmngtn" "blueste" "brdale"  "brkside" "clearcr" "collgcr" "crawfor"
 [8] "edwards" "gilbert" "greens"  "grnhill" "idotrr"  "landmrk" "meadowv"
[15] "mitchel" "names"   "noridge" "npkvill" "nridght" "nwames"  "oldtown"
[22] "sawyer"  "sawyerw" "somerst" "stonebr" "swisu"   "timber"  "veenker"
 [1] "020" "030" "040" "045" "050" "060" "070" "075" "080" "085" "090" "120"
[13] "150" "160" "180" "190"
[1] "no_bsmt" "fa"      "ta"      "gd"      "ex"     
[1] "n" "y"
[1] "no_fireplace" "po"           "fa"           "ta"           "gd"          
[6] "ex"

Categorical variable notes: Since tidy_responses adds x before numeric variables, we opted to tidy all character responses except ms_sub_class so we would not need to correct these labels later (there is nothing to tidy when responses are completely numeric). We notice that ms_sub_class is best considered nominal, quality variables are best considered ordinal , and that neighborhood has many levels that I might consider collapsing, but these types of conversions will occur during eda_modeling.

Train test split

Now that we have completed our data cleaning, we will split our data into train and test sets and save out the cleaned files. Since John held out a separate test set from the data we were given, your split will actually create our training and validation sets. We will use his holdout set as the test data.

Generate a train/test split

Assign 25% of the data to be our validation set. Stratify this split on the sale_price variable.

set.seed(12345)
splits <- ames_all |>  
  initial_split(prop = 3/4, strata = "sale_price", breaks = 4)

Save cleaned files

Save out cleaned train and validation sets as csv files and name them ames_clean_class_trn.csv and ames_clean_class_val.csv.

splits |> 
  analysis() |> 
  glimpse() |> 
  write_csv(here::here(path_data, "ames_clean_class_trn.csv"))

Rows: 1,465
Columns: 10
$ sale_price      <dbl> 105000, 126000, 115000, 127500, 120000, 99500, 125000,…
$ garage_area     <dbl> 730, 525, 0, 440, 308, 264, 264, 429, 539, 260, 0, 0, …
$ neighborhood    <fct> names, names, names, npkvill, npkvill, sawyerw, sawyer…
$ ms_sub_class    <fct> 020, 020, 020, 120, 120, 120, 120, 030, 090, 020, 020,…
$ total_bsmt_sf   <dbl> 882, 882, 864, 1069, 836, 918, 744, 816, 0, 1040, 950,…
$ bsmt_qual       <fct> ta, ta, ta, gd, gd, gd, gd, ta, no_bsmt, ta, ta, ta, t…
$ central_air     <fct> y, y, y, y, y, y, y, n, y, y, y, y, y, y, y, y, y, y, …
$ tot_rms_abv_grd <dbl> 5, 4, 5, 4, 4, 5, 4, 5, 8, 6, 6, 10, 4, 6, 6, 6, 5, 4,…
$ fireplaces      <dbl> 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ fireplace_qu    <fct> no_fireplace, no_fireplace, po, fa, no_fireplace, ta, …

splits |> 
  assessment() |> 
  glimpse() |> 
  write_csv(here::here(path_data, "ames_clean_class_val.csv"))

Rows: 490
Columns: 10
$ sale_price      <dbl> 189900, 105500, 306000, 222500, 355000, 221500, 204500…
$ garage_area     <dbl> 482, 320, 772, 434, 962, 880, 676, 678, 280, 762, 588,…
$ neighborhood    <fct> gilbert, brdale, nridght, gilbert, noridge, somerst, s…
$ ms_sub_class    <fct> 060, 160, 020, 060, 060, 020, 020, 060, 020, 060, 160,…
$ total_bsmt_sf   <dbl> 928, 525, 1704, 884, 1629, 1595, 1218, 991, 882, 1231,…
$ bsmt_qual       <fct> gd, ta, ex, gd, ex, gd, gd, gd, ta, gd, gd, gd, gd, ta…
$ central_air     <fct> y, y, y, y, y, y, y, y, y, y, y, y, y, y, y, y, y, y, …
$ tot_rms_abv_grd <dbl> 6, 6, 7, 8, 7, 6, 4, 8, 5, 9, 3, 4, 7, 7, 6, 7, 7, 8, …
$ fireplaces      <dbl> 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 2, 1, 0, …
$ fireplace_qu    <fct> ta, no_fireplace, gd, gd, ta, gd, no_fireplace, gd, no…