## 11.2 Case study: Seattle house prices

Kaggle.com is a machine learning and predictive modeling competition website that hosts datasets uploaded by companies, governmental organizations, and other individuals. One of their datasets is the “House Sales in King County, USA”. It consists of sale prices of homes sold between May 2014 and May 2015 in King County, Washington, USA, which includes the greater Seattle metropolitan area. This dataset is in the house_prices data frame included in the moderndive package.

The dataset consists of 21,613 houses and 21 variables describing these houses (for a full list and description of these variables, see the help file by running ?house_prices in the console). In this case study, we’ll create a multiple regression model where:

• The outcome variable $$y$$ is the sale price of houses.
• Two explanatory variables:
1. A numerical explanatory variable $$x_1$$: house size sqft_living as measured in square feet of living space. Note that 1 square foot is about 0.09 square meters.
2. A categorical explanatory variable $$x_2$$: house condition, a categorical variable with five levels where 1 indicates “poor” and 5 indicates “excellent.”

### 11.2.1 Exploratory data analysis: Part I

As we’ve said numerous times throughout this book, a crucial first step when presented with data is to perform an exploratory data analysis (EDA). Exploratory data analysis can give you a sense of your data, help identify issues with your data, bring to light any outliers, and help inform model construction.

Recall the three common steps in an exploratory data analysis we introduced in Subsection 5.1.1:

1. Looking at the raw data values.
2. Computing summary statistics.
3. Creating data visualizations.

First, let’s look at the raw data using View() to bring up RStudio’s spreadsheet viewer and the glimpse() function from the dplyr package:

View(house_prices)
glimpse(house_prices)
Rows: 21,613
Columns: 21
$id <chr> "7129300520", "6414100192", "5631500400", "2487200875",…$ date          <date> 2014-10-13, 2014-12-09, 2015-02-25, 2014-12-09, 2015-0…
$price <dbl> 221900, 538000, 180000, 604000, 510000, 1225000, 257500…$ bedrooms      <int> 3, 3, 2, 4, 3, 4, 3, 3, 3, 3, 3, 2, 3, 3, 5, 4, 3, 4, 2…
$bathrooms <dbl> 1.00, 2.25, 1.00, 3.00, 2.00, 4.50, 2.25, 1.50, 1.00, 2…$ sqft_living   <int> 1180, 2570, 770, 1960, 1680, 5420, 1715, 1060, 1780, 18…
$sqft_lot <int> 5650, 7242, 10000, 5000, 8080, 101930, 6819, 9711, 7470…$ floors        <dbl> 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, …
$waterfront <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…$ view          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0…
$condition <fct> 3, 3, 3, 5, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 4…$ grade         <fct> 7, 7, 6, 7, 8, 11, 7, 7, 7, 7, 8, 7, 7, 7, 7, 9, 7, 7, …
$sqft_above <int> 1180, 2170, 770, 1050, 1680, 3890, 1715, 1060, 1050, 18…$ sqft_basement <int> 0, 400, 0, 910, 0, 1530, 0, 0, 730, 0, 1700, 300, 0, 0,…
$yr_built <int> 1955, 1951, 1933, 1965, 1987, 2001, 1995, 1963, 1960, 2…$ yr_renovated  <int> 0, 1991, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$zipcode <fct> 98178, 98125, 98028, 98136, 98074, 98053, 98003, 98198,…$ lat           <dbl> 47.5, 47.7, 47.7, 47.5, 47.6, 47.7, 47.3, 47.4, 47.5, 4…
$long <dbl> -122, -122, -122, -122, -122, -122, -122, -122, -122, -…$ sqft_living15 <int> 1340, 1690, 2720, 1360, 1800, 4760, 2238, 1650, 1780, 2…
$sqft_lot15 <int> 5650, 7639, 8062, 5000, 7503, 101930, 6819, 9711, 8113,… Here are some questions you can ask yourself at this stage of an EDA: Which variables are numerical? Which are categorical? For the categorical variables, what are their levels? Besides the variables we’ll be using in our regression model, what other variables do you think would be useful to use in a model for house price? Observe, for example, that while the condition variable has values 1 through 5, these are saved in R as fct standing for “factors.” This is one of R’s ways of saving categorical variables. So you should think of these as the “labels” 1 through 5 and not the numerical values 1 through 5. Let’s now perform the second step in an EDA: computing summary statistics. Recall from Section 3.3 that summary statistics are single numerical values that summarize a large number of values. Examples of summary statistics include the mean, the median, the standard deviation, and various percentiles. We could do this using the summarize() function in the dplyr package along with R’s built-in summary functions, like mean() and median(). However, recall in Section 3.5, we saw the following code that computes a variety of summary statistics of the variable gain, which is the amount of time that a flight makes up mid-air: gain_summary <- flights %>% summarize( min = min(gain, na.rm = TRUE), q1 = quantile(gain, 0.25, na.rm = TRUE), median = quantile(gain, 0.5, na.rm = TRUE), q3 = quantile(gain, 0.75, na.rm = TRUE), max = max(gain, na.rm = TRUE), mean = mean(gain, na.rm = TRUE), sd = sd(gain, na.rm = TRUE), missing = sum(is.na(gain)) ) To repeat this for all three price, sqft_living, and condition variables would be tedious to code up. So instead, let’s use the convenient skim() function from the skimr package we first used in Subsection 6.1.1, being sure to only select() the variables of interest for our model: house_prices %>% select(price, sqft_living, condition) %>% skim() Skim summary statistics n obs: 21613 n variables: 3 ── Variable type:factor variable missing complete n n_unique top_counts ordered condition 0 21613 21613 5 3: 14031, 4: 5679, 5: 1701, 2: 172 FALSE ── Variable type:integer variable missing complete n mean sd p0 p25 p50 p75 p100 sqft_living 0 21613 21613 2079.9 918.44 290 1427 1910 2550 13540 ── Variable type:numeric variable missing complete n mean sd p0 p25 p50 p75 p100 price 0 21613 21613 540088.14 367127.2 75000 321950 450000 645000 7700000 Observe that the mean price of$540,088 is larger than the median of $450,000. This is because a small number of very expensive houses are inflating the average. In other words, there are “outlier” house prices in our dataset. (This fact will become even more apparent when we create our visualizations next.) However, the median is not as sensitive to such outlier house prices. This is why news about the real estate market generally report median house prices and not mean/average house prices. We say here that the median is more robust to outliers than the mean. Similarly, while both the standard deviation and interquartile-range (IQR) are both measures of spread and variability, the IQR is more robust to outliers. Let’s now perform the last of the three common steps in an exploratory data analysis: creating data visualizations. Let’s first create univariate visualizations. These are plots focusing on a single variable at a time. Since price and sqft_living are numerical variables, we can visualize their distributions using a geom_histogram() as seen in Section 2.5 on histograms. On the other hand, since condition is categorical, we can visualize its distribution using a geom_bar(). Recall from Section 2.8 on barplots that since condition is not “pre-counted”, we use a geom_bar() and not a geom_col(). # Histogram of house price: ggplot(house_prices, aes(x = price)) + geom_histogram(color = "white") + labs(x = "price (USD)", title = "House price") # Histogram of sqft_living: ggplot(house_prices, aes(x = sqft_living)) + geom_histogram(color = "white") + labs(x = "living space (square feet)", title = "House size") # Barplot of condition: ggplot(house_prices, aes(x = condition)) + geom_bar() + labs(x = "condition", title = "House condition") In Figure 11.3, we display all three of these visualizations at once. First, observe in the bottom plot that most houses are of condition “3”, with a few more of conditions “4” and “5”, and almost none that are “1” or “2”. Next, observe in the histogram for price in the top-left plot that a majority of houses are less than two million dollars. Observe also that the x-axis stretches out to 8 million dollars, even though there does not appear to be any houses close to that price. This is because there are a very small number of houses with prices closer to 8 million. These are the outlier house prices we mentioned earlier. We say that the variable price is right-skewed as exhibited by the long right tail. Further, observe in the histogram of sqft_living in the middle plot as well that most houses appear to have less than 5000 square feet of living space. For comparison, a football field in the US is about 57,600 square feet, whereas a standard soccer/association football field is about 64,000 square feet. Observe also that this variable is also right-skewed, although not as drastically as the price variable. For both the price and sqft_living variables, the right-skew makes distinguishing houses at the lower end of the x-axis hard. This is because the scale of the x-axis is compressed by the small number of quite expensive and immensely-sized houses. So what can we do about this skew? Let’s apply a log10 transformation to these variables. If you are unfamiliar with such transformations, we highly recommend you read Appendix A.3 on logarithmic (log) transformations. In summary, log transformations allow us to alter the scale of a variable to focus on multiplicative changes instead of additive changes. In other words, they shift the view to be on relative changes instead of absolute changes. Such multiplicative/relative changes are also called changes in orders of magnitude. Let’s create new log10 transformed versions of the right-skewed variable price and sqft_living using the mutate() function from Section 3.5, but we’ll give the latter the name log10_size, which is shorter and easier to understand than the name log10_sqft_living. house_prices <- house_prices %>% mutate( log10_price = log10(price), log10_size = log10(sqft_living) ) Let’s display the before and after effects of this transformation on these variables for only the first 10 rows of house_prices: house_prices %>% select(price, log10_price, sqft_living, log10_size) # A tibble: 21,613 x 4 price log10_price sqft_living log10_size <dbl> <dbl> <int> <dbl> 1 221900 5.34616 1180 3.07188 2 538000 5.73078 2570 3.40993 3 180000 5.25527 770 2.88649 4 604000 5.78104 1960 3.29226 5 510000 5.70757 1680 3.22531 6 1225000 6.08814 5420 3.73400 7 257500 5.41078 1715 3.23426 8 291850 5.46516 1060 3.02531 9 229500 5.36078 1780 3.25042 10 323000 5.50920 1890 3.27646 # … with 21,603 more rows Observe in particular the houses in the sixth and third rows. The house in the sixth row has price$1,225,000, which is just above one million dollars. Since $$10^6$$ is one million, its log10_price is around 6.09.

Contrast this with all other houses with log10_price less than six, since they all have price less than 1,000,000. The house in the third row is the only house with sqft_living less than 1000. Since $$1000 = 10^3$$, it’s the lone house with log10_size less than 3. Let’s now visualize the before and after effects of this transformation for price in Figure 11.4. # Before log10 transformation: ggplot(house_prices, aes(x = price)) + geom_histogram(color = "white") + labs(x = "price (USD)", title = "House price: Before") # After log10 transformation: ggplot(house_prices, aes(x = log10_price)) + geom_histogram(color = "white") + labs(x = "log10 price (USD)", title = "House price: After") Observe that after the transformation, the distribution is much less skewed, and in this case, more symmetric and more bell-shaped. Now you can more easily distinguish the lower priced houses. Let’s do the same for house size, where the variable sqft_living was log10 transformed to log10_size. # Before log10 transformation: ggplot(house_prices, aes(x = sqft_living)) + geom_histogram(color = "white") + labs(x = "living space (square feet)", title = "House size: Before") # After log10 transformation: ggplot(house_prices, aes(x = log10_size)) + geom_histogram(color = "white") + labs(x = "log10 living space (square feet)", title = "House size: After") Observe in Figure 11.5 that the log10 transformation has a similar effect of unskewing the variable. We emphasize that while in these two cases the resulting distributions are more symmetric and bell-shaped, this is not always necessarily the case. Given the now symmetric nature of log10_price and log10_size, we are going to revise our multiple regression model to use our new variables: 1. The outcome variable $$y$$ is the sale log10_price of houses. 2. Two explanatory variables: 1. A numerical explanatory variable $$x_1$$: house size log10_size as measured in log base 10 square feet of living space. 2. A categorical explanatory variable $$x_2$$: house condition, a categorical variable with five levels where 1 indicates “poor” and 5 indicates “excellent.” ### 11.2.2 Exploratory data analysis: Part II Let’s now continue our EDA by creating multivariate visualizations. Unlike the univariate histograms and barplot in the earlier Figures 11.3, 11.4, and 11.5, multivariate visualizations show relationships between more than one variable. This is an important step of an EDA to perform since the goal of modeling is to explore relationships between variables. Since our model involves a numerical outcome variable, a numerical explanatory variable, and a categorical explanatory variable, we are in a similar regression modeling situation as in Section 6.1 where we studied the UT Austin teaching scores dataset. Recall in that case the numerical outcome variable was teaching score, the numerical explanatory variable was instructor age, and the categorical explanatory variable was (binary) gender. We thus have two choices of models we can fit: either (1) an interaction model where the regression line for each condition level will have both a different slope and a different intercept or (2) a parallel slopes model where the regression line for each condition level will have the same slope but different intercepts. Recall from Subsection 6.1.3 that the geom_parallel_slopes() function is a special purpose function that Evgeni Chasnovski created and included in the moderndive package, since the geom_smooth() method in the ggplot2 package does not have a convenient way to plot parallel slopes models. We plot both resulting models in Figure 11.6, with the interaction model on the left. # Plot interaction model ggplot(house_prices, aes(x = log10_size, y = log10_price, col = condition)) + geom_point(alpha = 0.05) + geom_smooth(method = "lm", se = FALSE) + labs(y = "log10 price", x = "log10 size", title = "House prices in Seattle") # Plot parallel slopes model ggplot(house_prices, aes(x = log10_size, y = log10_price, col = condition)) + geom_point(alpha = 0.05) + geom_parallel_slopes(se = FALSE) + labs(y = "log10 price", x = "log10 size", title = "House prices in Seattle") In both cases, we see there is a positive relationship between house price and size, meaning as houses are larger, they tend to be more expensive. Furthermore, in both plots it seems that houses of condition 5 tend to be the most expensive for most house sizes as evidenced by the fact that the line for condition 5 is highest, followed by conditions 4 and 3. As for conditions 1 and 2, this pattern isn’t as clear. Recall from the univariate barplot of condition in Figure 11.3, there are only a few houses of condition 1 or 2. Let’s also show a faceted version of just the interaction model in Figure 11.7. It is now much more apparent just how few houses are of condition 1 or 2. ggplot(house_prices, aes(x = log10_size, y = log10_price, col = condition)) + geom_point(alpha = 0.4) + geom_smooth(method = "lm", se = FALSE) + labs(y = "log10 price", x = "log10 size", title = "House prices in Seattle") + facet_wrap(~ condition) Which exploratory visualization of the interaction model is better, the one in the left-hand plot of Figure 11.6 or the faceted version in Figure 11.7? There is no universal right answer. You need to make a choice depending on what you want to convey, and own that choice, with including and discussing both also as an option as needed. ### 11.2.3 Regression modeling Which of the two models in Figure 11.6 is “better”? The interaction model in the left-hand plot or the parallel slopes model in the right-hand plot? We had a similar discussion in Subsection 6.3.1 on model selection. Recall that we stated that we should only favor more complex models if the additional complexity is warranted. In this case, the more complex model is the interaction model since it considers five intercepts and five slopes total. This is in contrast to the parallel slopes model which considers five intercepts but only one common slope. Is the additional complexity of the interaction model warranted? Looking at the left-hand plot in Figure 11.6, we’re of the opinion that it is, as evidenced by the slight x-like pattern to some of the lines. Therefore, we’ll focus the rest of this analysis only on the interaction model. This visual approach is somewhat subjective, however, so feel free to disagree! What are the five different slopes and five different intercepts for the interaction model? We can obtain these values from the regression table. Recall our two-step process for getting the regression table: # Fit regression model: price_interaction <- lm(log10_price ~ log10_size * condition, data = house_prices) # Get regression table: get_regression_table(price_interaction) TABLE 11.1: Regression table for interaction model term estimate std_error statistic p_value lower_ci upper_ci intercept 3.330 0.451 7.380 0.000 2.446 4.215 log10_size 0.690 0.148 4.652 0.000 0.399 0.980 condition2 0.047 0.498 0.094 0.925 -0.930 1.024 condition3 -0.367 0.452 -0.812 0.417 -1.253 0.519 condition4 -0.398 0.453 -0.879 0.380 -1.286 0.490 condition5 -0.883 0.457 -1.931 0.053 -1.779 0.013 log10_size:condition2 -0.024 0.163 -0.148 0.882 -0.344 0.295 log10_size:condition3 0.133 0.148 0.893 0.372 -0.158 0.424 log10_size:condition4 0.146 0.149 0.979 0.328 -0.146 0.437 log10_size:condition5 0.310 0.150 2.067 0.039 0.016 0.604 Recall we saw in Subsection 6.1.2 how to interpret a regression table when there are both numerical and categorical explanatory variables. Let’s now do the same for all 10 values in the estimate column of Table 11.1. In this case, the “baseline for comparison” group for the categorical variable condition are the condition 1 houses, since “1” comes first alphanumerically. Thus, the intercept and log10_size values are the intercept and slope for log10_size for this baseline group. Next, the condition2 through condition5 terms are the offsets in intercepts relative to the condition 1 intercept. Finally, the log10_size:condition2 through log10_size:condition5 are the offsets in slopes for log10_size relative to the condition 1 slope for log10_size. Let’s simplify this by writing out the equation of each of the five regression lines using these 10 estimate values. We’ll write out each line in the following format: $\widehat{\log10(\text{price})} = \hat{\beta}_0 + \hat{\beta}_{\text{size}} \cdot \log10(\text{size})$ 1. Condition 1: $\widehat{\log10(\text{price})} = 3.33 + 0.69 \cdot \log10(\text{size})$ 1. Condition 2: \begin{aligned} \widehat{\log10(\text{price})} &= (3.33 + 0.047) + (0.69 - 0.024) \cdot \log10(\text{size}) \\ &= 3.377 + 0.666 \cdot \log10(\text{size}) \end{aligned} 1. Condition 3: \begin{aligned} \widehat{\log10(\text{price})} &= (3.33 - 0.367) + (0.69 + 0.133) \cdot \log10(\text{size}) \\ &= 2.963 + 0.823 \cdot \log10(\text{size}) \end{aligned} 1. Condition 4: \begin{aligned} \widehat{\log10(\text{price})} &= (3.33 - 0.398) + (0.69 + 0.146) \cdot \log10(\text{size}) \\ &= 2.932 + 0.836 \cdot \log10(\text{size}) \end{aligned} 1. Condition 5: \begin{aligned} \widehat{\log10(\text{price})} &= (3.33 - 0.883) + (0.69 + 0.31) \cdot \log10(\text{size}) \\ &= 2.447 + 1 \cdot \log10(\text{size}) \end{aligned} These correspond to the regression lines in the left-hand plot of Figure 11.6 and the faceted plot in Figure 11.7. For homes of all five condition types, as the size of the house increases, the price increases. This is what most would expect. However, the rate of increase of price with size is fastest for the homes with conditions 3, 4, and 5 of 0.823, 0.836, and 1, respectively. These are the three largest slopes out of the five. ### 11.2.4 Making predictions Say you’re a realtor and someone calls you asking you how much their home will sell for. They tell you that it’s in condition = 5 and is sized 1900 square feet. What do you tell them? Let’s use the interaction model we fit to make predictions! We first make this prediction visually in Figure 11.8. The predicted log10_price of this house is marked with a black dot. This is where the following two lines intersect: • The regression line for the condition = 5 homes and • The vertical dashed black line at log10_size equals 3.28, since our predictor variable is the log10 transformed square feet of living space of $$\log10(1900) = 3.28$$. Eyeballing it, it seems the predicted log10_price seems to be around 5.75. Let’s now obtain the exact numerical value for the prediction using the equation of the regression line for the condition = 5 houses, being sure to log10() the square footage first. 2.45 + 1 * log10(1900) [1] 5.73 This value is very close to our earlier visually made prediction of 5.75. But wait! Is our prediction for the price of this house5.75? No! Remember that we are using log10_price as our outcome variable! So, if we want a prediction in dollar units of price, we need to unlog this by taking a power of 10 as described in Appendix A.3.

10^(2.45 + 1 * log10(1900))
[1] 535493

So our predicted price for this home of condition 5 and of size 1900 square feet is $535,493. Learning check (LC11.1) Repeat the regression modeling in Subsection 11.2.3 and the prediction making you just did on the house of condition 5 and size 1900 square feet in Subsection 11.2.4, but using the parallel slopes model you visualized in Figure 11.6. Show that it’s$524,807!