This is a challenging dataset, in part because it’s real and messy. I will guide you through a simplified sensible analysis, but other models are possible.

Note that I needed to set cache=FALSE to assure all output was updated.

1 ANCOVA model: Albuquerque NM 87108, House and Apartment listing prices

Prof Erhardt constructed a dataset of listing prices for dwellings (homes and apartments) for sale from on Feb 26, 2016 at 1 PM for Albuquerque NM 87108. In this assignment we’ll develop a model to help understand which qualities that contribute to a typical dwelling’s listing price. We will then also predict the listing prices of new listings posted on the following day, Feb 27, 2016 by 2 PM.

Because we want to model a typical dwelling, it is completely reasonable to remove “unusual” dwellings from the dataset. Dwellings are a distribution that has a long tail!

1.1 Unusual assignment, not top-down, but up-down-up-down

This is an unusual assignment because the workflow of this assignment isn’t top-down; instead, you’ll be scrolling up and down as you make decisions about the data and model you’re fitting. Yes, I have much of the code worked out for you. However, there are data decisions to make early in the code (such as excluding observations, transforming variables, etc.) that depend on the analysis (model checking) later. Think of it as a “choose your own adventure” that I’ve chosen for you.

1.1.1 Keep a record of your decisions

It is always desirable to make your work reproducible, either by someone else or by your future self. For each step you take, keep a diary of (a) what the next minor goal is, (b) what evidence/information you have, (c) what decision you make, and (d) what the outcome was.

For example, here’s the first couple entries of your diary:

  1. Include only “typical dwellings”. Based on scatterplot, remove extreme observations. Keep only HOUSE and APARTMENT.
  2. Exclude a few variables to reduce multicollinearity between predictor variables. Exclude Baths and LotSize.

1.2 (1 p) (Step 1) Restrict data to “typical” dwellings

Step 1: After looking at the scatterplot below, identify what you consider to be a “typical dwelling” and exclude observations far from that range. For example, there are only a couple TypeSale that are common enough to model; remember to run factor() again to remove factor levels that no longer appear.

# read the data, skip the first two comment lines of the data file
dat.abq <- read.csv(""
                , skip=2, stringsAsFactors = FALSE)[,-1]
dat.abq$TypeSale <- factor(dat.abq$TypeSale)

# To help scale the intercept to a more reasonable value
#   Scaling the x-variables are sometimes done to the mean of each x.
# center year at 1900
dat.abq$YearBuilt_1900 <- dat.abq$YearBuilt - 1900
dat.abq$YearBuilt <- NULL

   TypeSale PriceList Beds Baths Size.sqft LotSize DaysListed
1     HOUSE    186900    3     2      1305    6969          0
2 APARTMENT    305000    1     1      2523    6098          0
3 APARTMENT    244000    1     1      2816    6098          0
4     CONDO    108000    3     2      1137      NA          0
5     CONDO     64900    2     1      1000      NA          1
6     HOUSE    275000    3     3      2022    6098          1
1             54
2             48
3             89
4             96
5             85
6             52

# dat.abq <- subset(dat.abq, !(X > Z))   # exclude observations of this type
# dat.abq <- dat.abq[!(dat.abq$X > Z),]  # exclude observations of this type

  # these deletions are based only on the scatter plot in order to have
  #  "typical" dwellings

1.3 (1 p) (Step 3) Transform response, if necessary.

Step 3: Does the response variable require a transformation? If so, what transformation is recommended from the model diagnostic plots (Box-Cox)?

1.3.1 Solution


# Price in units of $1000
dat.abq$PriceListK <- dat.abq$PriceList / 1000
dat.abq$PriceList <- NULL


1.4 (1 p) (Step 4) Remove extremely influential observations.

Step 4: The goal is to develop a model that will work well for the typical dwellings. If an observation is highly influential, then it’s unusual.

## Remove influential observation
#dat.abq <- dat.abq[-which(row.names(dat.abq) %in% c(...)),]


1.5 Subset data for model building and prediction

Create a subset of the data for building the model, and another subset for prediction later on.

# remove observations with NAs
dat.abq <- na.omit(dat.abq)

# the data subset we will use to build our model
dat.sub <- subset(dat.abq, DaysListed > 0)

# the data subset we will predict from our model
dat.pred <- subset(dat.abq, DaysListed == 0)
# the prices we hope to predict well from our model
dat.pred$PriceListK_true <- dat.pred$PriceListK
# set them to NA to predict them later
dat.pred$PriceListK <- NA

Scatterplot of the model-building subset.

# NOTE, this plot takes a long time if you're repeadly recompiling the document.
# comment the "print(p)" line so save some time when you're not evaluating this plot.
p <- ggpairs(dat.sub
            , mapping = ggplot2::aes(colour = TypeSale, alpha = 0.5)
            , lower = list(continuous = "points")
            , upper = list(continuous = "cor")

There are clearly some unusual observations. Go back to the first code chunk and remove some observations that don’t represent a “typical” dwelling.

For example, remove these dwellings (in code above):

  • Super expensive dwelling
  • Dwellings with huge lots
  • Dwellings that were listed for years
  • Because most dwellings were APARTMENTs and HOUSEs, remove the others (there are only 1 or so of each).

Discuss the observed correlations or other outstanding features in the data.

1.5.1 Solution


1.6 (1 p) (Step 2) Fit full two-way interaction model.

You’ll revisit this section after each modification of the data above.

Step 2: Let’s fit the full two-way interaction model and assess the assumptions. However, some of the predictor variables are highly correlated. Recall that the interpretation of a beta coefficient is “the expected increase in the response for a 1-unit increase in \(x\) with all other predictors held constant”. It’s hard to hold one variable constant if it’s correlated with another variable you’re increasing. Therefore, we’ll make a decision to retain some variables but not others depending on their correlation values. (In the PCA chapter, we’ll see another strategy.)

Somewhat arbitrarily, let’s exclude Baths (since highly correlated with Beds and Size.sqft). Let’s also exclude LotSize (since highly correlated with Size.sqft). Modify the code below. Notice that because APARTMENTs don’t have more than 1 Beds or Baths, those interaction terms need to be excluded from the model; I show you how to do this manually using the update() function.

Note that the formula below y ~ (x1 + x2 + x3)^2 expands into all main effects and two-way interactions.

lm.full <- lm(PriceListK ~ (TypeSale + Beds + Baths + Size.sqft + LotSize + DaysListed + YearBuilt_1900)^2, data = dat.sub)
#lm.full <- lm(PriceListK ~ (Beds + Baths + Size.sqft + LotSize + DaysListed + YearBuilt_1900)^2, data = dat.sub)
try(Anova(lm.full, type=3))
## Note that this doesn't work because APARTMENTs only have 1 bed and 1 bath.
## There isn't a second level of bed or bath to estimate the interaction.
## Therefore, remove those two terms
lm.full <- update(lm.full, . ~ . - TypeSale:Beds - TypeSale:Baths)
try(Anova(lm.full, type=3))

Note the diagnostic plots below are printed automatically with a new function below (echo=FALSE, so look in Rmd file). Thanks to Eric Kruger for the idea of a diagnostic plotting function.

## Uncomment this line when you're ready to assess the model assumptions
#lm.diag.plots(lm.full, rc.mfrow = c(1,3))

After Step 2, interpret the residual plots. What are the primary issues?

1.6.1 Solution


1.7 (1 p) (Step 5) Model selection, check model assumptions.

Using step(..., direction="both") with the BIC criterion, perform model selection.

1.7.1 Solution

## BIC
# option: test="F" includes additional information
#           for parameter estimate tests that we're familiar with
# option: for BIC, include k=log(nrow( [data.frame name] )) <- step(lm.full, direction="both", test="F", trace = 0
                         , k=log(nrow(dat.sub))) <-

## Uncomment this line when you're ready to assess the model assumptions
#lm.diag.plots(, rc.mfrow = c(1,3))

Model assumptions appear to be reasonably met. A few influential observations exist.

1.8 (2 p) (Step 6) Plot final model, interpret coefficients.

If you arrived at the same model I did, then the code below will plot it. Eventually (after Step 7), the fitted model equations will describe the each dwelling TypeSale and interpret the coefficients.

#Anova(, type=3)

lm(formula = PriceListK ~ TypeSale + Beds + Baths + Size.sqft + 
    LotSize + YearBuilt_1900 + TypeSale:LotSize + TypeSale:YearBuilt_1900 + 
    Beds:Baths + Beds:Size.sqft + Beds:LotSize + Baths:YearBuilt_1900 + 
    Size.sqft:LotSize, data = dat.sub)

     Min       1Q   Median       3Q      Max 
-174.692  -21.403   -0.624   23.065  133.337 

Coefficients: (2 not defined because of singularities)
                                           Estimate Std. Error t value
(Intercept)                               8.404e+01  7.847e+01   1.071
TypeSaleFOR SALE BY OWNER                 1.071e+02  6.729e+01   1.591
TypeSaleFORECLOSURE                       5.796e+02  2.456e+02   2.360
TypeSaleHOUSE                             6.191e+02  1.162e+02   5.329
Beds                                     -6.977e+01  2.939e+01  -2.374
Baths                                     3.718e+01  5.579e+01   0.667
Size.sqft                                -4.852e-02  1.503e-02  -3.228
LotSize                                   7.109e-03  5.577e-03   1.275
YearBuilt_1900                           -3.434e+00  1.213e+00  -2.831
TypeSaleFOR SALE BY OWNER:LotSize                NA         NA      NA
TypeSaleFORECLOSURE:LotSize              -3.183e-02  1.629e-02  -1.954
TypeSaleHOUSE:LotSize                    -4.710e-02  9.702e-03  -4.854
TypeSaleFOR SALE BY OWNER:YearBuilt_1900         NA         NA      NA
TypeSaleFORECLOSURE:YearBuilt_1900       -8.471e+00  4.862e+00  -1.742
TypeSaleHOUSE:YearBuilt_1900             -5.598e+00  1.694e+00  -3.304
Beds:Baths                               -7.267e+01  1.318e+01  -5.514
Beds:Size.sqft                            4.958e-02  6.367e-03   7.787
Beds:LotSize                              1.245e-02  4.525e-03   2.751
Baths:YearBuilt_1900                      3.647e+00  8.460e-01   4.310
Size.sqft:LotSize                         1.057e-06  1.784e-07   5.921
(Intercept)                               0.28686    
TypeSaleFOR SALE BY OWNER                 0.11486    
TypeSaleFORECLOSURE                       0.02029 *  
TypeSaleHOUSE                            6.52e-07 ***
Beds                                      0.01960 *  
Baths                                     0.50666    
Size.sqft                                 0.00171 ** 
LotSize                                   0.20547    
YearBuilt_1900                            0.00565 ** 
TypeSaleFOR SALE BY OWNER:LotSize              NA    
TypeSaleFORECLOSURE:LotSize               0.05362 .  
TypeSaleHOUSE:LotSize                    4.68e-06 ***
TypeSaleFOR SALE BY OWNER:YearBuilt_1900       NA    
TypeSaleFORECLOSURE:YearBuilt_1900        0.08469 .  
TypeSaleHOUSE:YearBuilt_1900              0.00134 ** 
Beds:Baths                               2.96e-07 ***
Beds:Size.sqft                           8.01e-12 ***
Beds:LotSize                              0.00711 ** 
Baths:YearBuilt_1900                     3.94e-05 ***
Size.sqft:LotSize                        4.96e-08 ***
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 54.98 on 96 degrees of freedom
Multiple R-squared:  0.9701,    Adjusted R-squared:  0.9648 
F-statistic: 182.9 on 17 and 96 DF,  p-value: < 2.2e-16

Fitted model equation is \[ \widehat{\log_{10}(\textrm{PriceList})} = 84 + 107 I(\textrm{TypeSale} = \textrm{HOUSE}) + 580 \log_{10}(\textrm{Size.sqft}) \]

1.8.1 Solution

After Step 7, return and intepret the model coefficients above.


1.9 (1 p) (Step 7) Transform predictors.

We now have enough information to see that a transformation of a predictor can be useful. See the curvature with Size.sqft? This is one of the headaches of regression modelling, everything depends on everything else and you learn as you go. Return to the top and transform Size.sqft and LotSize.

A nice feature of this transformation is that the model interaction goes away. Our interpretation is now on the log scale, but it’s a simpler model.

1.10 (2 p) (Step 8) Predict new observations, interpret model’s predictive ability.

Using the predict() function, we’ll input the data we held out to predict earlier, and use our final model to predict the PriceListK response. Note that 10^lm.pred is the table of values on the scale of “thousands of dollars”.

Interpret the predictions below the output.

How well do you expect this model to predict? Justify your answer.

# predict new observations, convert to data frame
lm.pred <-, newdata = dat.pred, interval = "prediction"))
# add column of actual list prices
lm.pred$PriceListK <- dat.pred$PriceListK_true
       fit      lwr      upr PriceListK
1 160.2250 49.43439 271.0157      186.9
2 127.1842 12.81698 241.5515      305.0
3 138.1019 17.26725 258.9365      244.0
# on "thousands of dollars" scale
            fit          lwr           upr    PriceListK
1 1.678934e+160 2.718868e+49 1.036763e+271 7.943282e+186
2 1.528426e+127 6.561084e+12 3.560519e+241 1.000000e+305
3 1.264370e+138 1.850337e+17 8.639680e+258 1.000000e+244
# attributes of the three predicted observations
   TypeSale Beds Baths Size.sqft LotSize DaysListed YearBuilt_1900
1     HOUSE    3     2      1305    6969          0             54
2 APARTMENT    1     1      2523    6098          0             48
3 APARTMENT    1     1      2816    6098          0             89
  PriceListK PriceListK_true
1         NA           186.9
2         NA           305.0
3         NA           244.0

1.10.1 Solution